Computational analysis of antimicrobial peptides targeting key receptors in infection-related cardiovascular diseases: Molecular docking and dynamics insights

Computational analysis of antimicrobial peptides targeting key receptors in infection-related cardiovascular diseases: Molecular docking and dynamics insights | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Computational analysis of antimicrobial peptides targeting key receptors in infection-related cardiovascular diseases: Molecular docking and dynamics insights Doni Dermawan, Nasser Alotaiq This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5376324/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Infection-related cardiovascular diseases (CVDs) pose a significant health challenge, driving the need for novel therapeutic strategies to target key receptors involved in inflammation and infection. Antimicrobial peptides (AMPs) show the potential to disrupt pathogenic processes and offer a promising approach to CVD treatment. This study investigates the binding potential of selected AMPs with critical receptors implicated in CVDs, aiming to explore their therapeutic potential. A comprehensive computational approach was employed to assess AMP interactions with CVD-related receptors, including ACE2, CRP, MMP9, NLRP3, and TLR4. Molecular docking studies identified AMPs with high binding affinities to these targets, notably Tachystatin, Pleurocidin, and Subtilisin A, which showed strong interactions with ACE2, CRP, and MMP9. Following docking, 100 ns molecular dynamics (MD) simulations confirmed the stability of AMP-receptor complexes, and MM/PBSA calculations provided quantitative insights into binding energies, underscoring the potential of these AMPs to modulate receptor activity in infection and inflammation contexts. The study highlights the therapeutic potential of Tachystatin, Pleurocidin, and Subtilisin A in targeting infection-related pathways in CVDs. These AMPs demonstrate promising receptor binding properties and stability in computational models. Future research should focus on in vitro and in vivo studies to confirm their efficacy and safety, paving the way for potential clinical applications in managing infection-related cardiovascular conditions. Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Health sciences/Molecular medicine Antimicrobial peptides Cardiovascular disease Molecular docking Molecular dynamics Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Infections and cardiovascular diseases (CVDs) share a complex, bidirectional relationship. Pathogenic microorganisms can initiate or exacerbate CVDs through direct infection of cardiovascular tissues, immune system dysregulation, or the induction of chronic inflammatory states 1 , 2 . For instance, respiratory pathogens such as influenza virus and bacterial pathogens like Chlamydia pneumoniae and Helicobacter pylori have been associated with an increased risk of myocardial infarction and atherosclerosis 3 , 4 . The chronic inflammation induced by these pathogens accelerates the formation of atherosclerotic plaques, a major cause of coronary artery disease. Among the most prominent mechanisms is the activation of the innate immune system via receptors such as Toll-Like Receptors (TLRs), which recognize pathogen-associated molecular patterns (PAMPs) 5 – 7 . TLRs initiate signaling cascades upon activation that produce pro-inflammatory cytokines, resulting in endothelial dysfunction and plaque instability. Another key player in this inflammatory process is the NLRP3 inflammasome, which responds to infectious and non-infectious stimuli, including viral RNA, bacterial toxins, and cholesterol crystals 8 , 9 . Once activated, the NLRP3 inflammasome promotes the secretion of IL-1β, a potent pro-inflammatory cytokine implicated in the progression of atherosclerosis 10 , 11 . Furthermore, angiotensin-converting enzyme 2 (ACE2), a receptor known for regulating blood pressure, has gained considerable attention due to its interaction with the SARS-CoV-2 virus 12 , 13 . The binding of the virus to ACE2 receptors impairs its physiological functions, leading to cardiovascular complications, including myocardial injury and arrhythmias 14 . Matrix metalloproteinases (MMPs), a family of proteolytic enzymes involved in extracellular matrix remodeling, also play a critical role in CVD progression, particularly in the degradation of the fibrous cap of atherosclerotic plaques, leading to plaque rupture and subsequent cardiovascular events 15 , 16 . While MMP9 is primarily recognized as a target enzyme, it may also exhibit receptor-like characteristics through its interactions with various signaling molecules, highlighting its dual role in inflammation and cardiovascular pathology. Treating infections related to cardiovascular diseases typically involves using antimicrobial agents such as antibiotics, antivirals, and antifungals, depending on the pathogen involved. For bacterial infections, antibiotics such as macrolides and β-lactams are commonly prescribed. For example, azithromycin is often used to treat Chlamydia pneumoniae infections associated with atherosclerosis 17 , 18 . However, the use of antibiotics poses several challenges, including the emergence of antibiotic resistance, which has become a significant global health threat. The overuse and misuse of antibiotics have led to the development of multidrug-resistant (MDR) strains, which are not only harder to treat but also contribute to higher mortality rates in patients with CVDs 19 – 21 . Moreover, the long-term use of antibiotics has been associated with adverse cardiovascular outcomes, including arrhythmias and QT interval prolongation 22 . As a result, there is growing interest in exploring alternative therapeutic strategies, such as the use of antimicrobial peptides (AMPs), which have the potential to overcome the limitations of current therapies and provide more targeted interventions. One of the significant advantages of AMPs is their ability to selectively target microbial membranes, which reduces the likelihood of developing resistance compared to traditional antibiotics 23 , 24 . Unlike conventional antimicrobial agents that often target specific bacterial proteins or enzymes, AMPs disrupt microbial membranes by interacting with their lipid bilayers, leading to cell lysis and death 25 . This mode of action is less prone to resistance, as microbes would need to undergo significant changes in membrane composition to evade AMP activity 26 , 27 . This study aimed to explore the potential of AMPs to bind with key receptors involved in infection-related CVDs, offering insights into their role in modulating receptor interactions. By employing a comprehensive in silico approach, including molecular docking and molecular dynamics simulations, we assessed the interaction dynamics, stability, and binding affinities of various AMPs with critical CVD-related receptors. This study aims to provide insights into the potential role of AMPs in modulating receptor interactions, which may pave the way for the development of novel peptide-based therapies targeting infection-driven cardiovascular and inflammatory conditions. Results Molecular Docking Simulations of AMPs and Receptors Implicated in Infection-related CVDs The best binding poses of AMPs and ACE2 (as one of the target receptors) are presented in Fig. 1 . The HADDOCK score provided an overall measure of the docking quality by integrating both the spatial and energetic fit of the AMP to the receptor. Free binding energy, measured in kilocalories per mole, offered a quantitative assessment of the binding strength between the AMPs and the receptors 28 . The van der Waals and electrostatic energies were also analyzed to gain insight into the non-covalent forces driving the interactions, which are crucial for understanding how these peptides interact at the molecular level. Additionally, desolvation energy, which reflects the energy of displacing water molecules from the receptor surface upon peptide binding, was calculated to understand the binding thermodynamics 29 . These results laid the groundwork for further molecular dynamics simulations to explore the stability and dynamics of the AMP-receptor complexes. Figure 2 presents the free binding energy (kcal/mol) scores for the top-performing AMPs interacting with different target proteins, focusing on Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A as consistent performers. Tachystatin demonstrated significant binding affinity across multiple targets. When docked with ACE2, Tachystatin achieved a HADDOCK score of -102.0 ± 3.7 a.u. and a binding energy of -10.7 kcal/mol, outperforming the standard inhibitor DX600 (-8.6 kcal/mol). The van der Waals energy was − 48.4 ± 4.4 kcal/mol, and the electrostatic energy was − 220.9 ± 32.4 kcal/mol, reflecting strong interaction forces, while the RMSD value of 1.5 ± 0.0 Å indicates a relatively stable conformation. Tachystatin similarly showed strong binding with MMP9, achieving a HADDOCK score of -136.4 ± 3.4 a.u. and a binding energy of -12.2 kcal/mol. These results highlight Tachystatin’s consistent performance across different proteins. This interaction was considered strong based on comparative analysis with the standard inhibitor results, which provided a benchmark for evaluating the binding affinity and interaction strength of the AMPs. Thermolysin was also identified as a highly effective AMP, particularly with ACE2 and NLRP3. In the ACE2 complex, Thermolysin achieved a HADDOCK score of -91.4 ± 2.4 a.u. and a binding energy of -10.7 kcal/mol, comparable to Tachystatin. Its van der Waals energy of -49.2 ± 2.5 kcal/mol and electrostatic energy of -131.8 ± 20.5 kcal/mol indicate a well-balanced interaction. Thermolysin’s performance with MMP9 also stood out, yielding a binding energy of -10.6 kcal/mol with a HADDOCK score of -114.4 ± 3.8 a.u., and it demonstrated a stable RMSD of 1.2 ± 0.2 Å. Pleurocidin, another top-performing peptide, demonstrated robust binding across multiple proteins. In complex with ACE2, Pleurocidin achieved a HADDOCK score of -104.8 ± 1.9 a.u. and a binding energy of -11.2 kcal/mol, with a van der Waals energy of -46.6 ± 6.0 kcal/mol and an electrostatic energy of -220.6 ± 23.5 kcal/mol, indicating strong non-covalent interactions. Similarly, Pleurocidin exhibited a strong interaction with MMP9, with a HADDOCK score of -143.7 ± 4.0 a.u. and a binding energy of -9.7 kcal/mol. The stability of these interactions was underscored by an RMSD of 1.2 ± 0.1 Å. Subtilisin A demonstrated strong binding interactions, particularly with CRP and NLRP3. In the CRP complex, Subtilisin A achieved a HADDOCK score of -128.1 ± 8.3 a.u. and a binding energy of -12.0 kcal/mol, significantly surpassing the binding energy of DX600 (-9.7 kcal/mol). The electrostatic energy was − 306.9 ± 57.8 kcal/mol, indicating powerful electrostatic interactions, while the RMSD value of 0.6 ± 0.5 Å reflects a stable interaction. In the NLRP3 complex, Subtilisin A showed similar effectiveness, with a HADDOCK score of -138.7 ± 2.8 a.u. and a binding energy of -12.1 kcal/mol, demonstrating its strong and consistent performance. The results, as summarized in Table 1 , demonstrate the strong binding affinity and stability of the top-performing AMPs—particularly Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A—toward key protein targets implicated in infection pathways related to CVDs. These AMPs exhibit the potential to inhibit receptor-mediated adhesion and signaling processes, which play a critical role in infection onset and progression. Their stable interactions with these receptors suggest promising therapeutic applications in preventing CVD-associated infections. Detailed molecular docking results are available in Supplementary Data 1. Table 1 Molecular docking results of top 5 performing target protein-AMP complexes compared to the standard inhibitor (DX600 peptide). Complex HADDOCK score (a.u.) Binding energy (kcal/mol) Van der Waals energy Electrostatic energy Desolvation energy RMSD ACE2 Complexes ACE2:DX600 peptide (standard inhibitor) -76.8 +/- 0.9 -8.6 -38.7 +/- 2.9 -79.0 +/- 28.3 -26.1 +/- 4.2 1.4 +/- 0.2 ACE2:Hepcidin -96.3 +/- 7.2 -11.3 -44.7 +/- 1.2 -250.1 +/- 24.0 -3.1 +/- 4.7 0.5 +/- 0.3 ACE2:Pleurocidin -104.8 +/- 1.9 -11.2 -46.6 +/- 6.0 -220.6 +/- 23.5 -18.0 +/- 2.2 1.5 +/- 0.1 ACE2:Tachystatin -102.0 +/- 3.7 -10.7 -48.4 +/- 4.4 -220.9 +/- 32.4 -20.0 +/- 4.3 1.5 +/- 0.0 ACE2:Thermolysin -91.4 +/- 2.4 -10.7 -49.2 +/- 2.5 -131.8 +/- 20.5 -19.0 +/- 1.1 0.7 +/- 0.5 ACE2:PvHCt -94.0 +/- 2.8 -9.9 -44.8 +/- 2.2 -117.5 +/- 15.6 -28.6 +/- 1.0 0.6 +/- 0.4 CRP Complexes CRP:DX600 peptide (standard inhibitor) -90.7 +/- 8.8 -9.7 -40.8 +/- 4.6 -171.9 +/- 13.0 -15.7 +/- 2.1 0.8 +/- 0.5 CRP:Nisin -79.3 +/- 4.1 -13.1 -43.0 +/- 4.4 -198.5 +/- 14.5 3.1 +/- 1.3 2.6 +/- 0.6 CRP:Subtilisin A -128.1 +/- 8.3 -12.0 -46.5 +/- 6.1 -306.9 +/- 57.8 -22.6 +/- 7.6 0.6 +/- 0.5 CRP:Protegrin-1 -92.7 +/- 4.0 -11.3 -37.5 +/- 3.9 -330.3 +/- 21.8 9.7 +/- 2.4 1.3 +/- 0.1 CRP:Pardaxin -102.4 +/- 2.9 -11.0 -50.1 +/- 0.3 -202.1 +/- 37.0 -16.5 +/- 5.7 1.7 +/- 0.2 CRP:Magainin -108.2 +/- 5.6 -10.6 -25.2 +/- 4.2 -352.4 +/- 45.0 -14.6 +/- 1.7 1.0 +/- 0.2 MMP9 Complexes MMP9:DX600 peptide (standard inhibitor) -98.3 +/- 2.5 -9.4 -55.5 +/- 3.8 -205.3 +/- 34.7 -22.0 +/- 2.3 1.4 +/- 0.3 MMP9:Tachystatin -136.4 +/- 3.4 -12.2 -85.2 +/- 7.8 -235.8 +/- 14.4 -28.6 +/- 3.1 1.6 +/- 0.0 MMP9:Thermolysin -114.4 +/- 3.8 -10.6 -70.3 +/- 6.6 -173.2 +/- 46.2 -27.6 +/- 3.6 1.2 +/- 0.2 MMP9:Beta-defensin 2 -93.6 +/- 4.7 -10.0 -55.2 +/- 3.8 -285.5 +/- 37.5 -3.9 +/- 1.5 0.5 +/- 0.3 MMP9:Exendin-4 -48.2 +/- 3.2 -9.8 -47.3 +/- 5.7 -55.4 +/- 20.3 -14.5 +/- 1.5 0.9 +/- 0.5 MMP9:Pleurocidin -143.7 +/- 4.0 -9.7 -62.7 +/- 6.7 -406.5 +/- 40.9 -27.8 +/- 2.8 1.2 +/- 0.1 NLRP3 Complexes NLRP3:DX600 peptide (standard inhibitor) -87.9 +/- 2.7 -10.5 -50.1 +/- 6.8 -237.3 +/- 44.7 -5.9 +/- 4.6 0.9 +/- 0.5 NLRP3:Subtilisin A -138.7 +/- 2.8 -12.1 -75.8 +/- 4.3 -271.9 +/- 59.6 -31.2 +/- 6.0 0.7 +/- 0.4 NLRP3:Dermcidin -98.6 +/- 14.7 -12.0 -45.9 +/- 4.1 -374.3 +/- 87.9 11.9 +/- 4.2 1.0 +/- 0.6 NLRP3:Tachystatin -100.1 +/- 8.7 -11.3 -50.4 +/- 9.3 -281.3 +/- 36.1 -0.2 +/- 5.0 1.4 +/- 0.2 NLRP3:Thermolysin -82.3 +/- 4.9 -11.3 -51.1 +/- 4.7 -193.6 +/- 19.4 0.3 +/- 2.8 1.2 +/- 0.9 NLRP3:Pleurocidin -110.5 +/- 9.2 -11.0 -63.0 +/- 6.9 -253.8 +/- 21.7 -15.0 +/- 6.3 1.1 +/- 0.7 TLR4 Complexes TLR4:DX600 peptide (standard inhibitor) -78.4 +/- 4.4 -12.9 -41.0 +/- 7.0 -111.4 +/- 34.1 -22.9 +/- 5.1 1.3 +/- 0.2 TLR4:Tachystatin -125.7 +/- 2.9 -14.7 -71.8 +/- 3.2 -172.7 +/- 15.0 -23.1 +/- 2.0 0.4 +/- 0.2 TLR4:Dermcidin -91.1 +/- 8.6 -14.3 -57.5 +/- 3.3 -137.5 +/- 37.3 -10.2 +/- 1.9 1.0 +/- 0.6 TLR4:Subtilisin A -108.7 +/- 3.4 -13.6 -59.9 +/- 2.4 -70.5 +/- 21.6 -40.4 +/- 3.5 1.3 +/- 0.1 TLR4:Nisin -94.6 +/- 6.3 -13.5 -55.8 +/- 5.0 -153.0 +/- 28.9 -13.6 +/- 3.1 1.3 +/- 0.1 TLR4:Chim2 -93.3 +/- 4.4 -13.3 -47.7 +/- 5.0 -119.0 +/- 23.8 -27.3 +/- 5.7 0.4 +/- 0.3 The correlation matrix depicted in Fig. 3 provides a detailed analysis of the interplay between different energy components—van der Waals energy, electrostatic energy, and desolvation energy—and their contributions to the binding energy of AMP-receptor complexes, specifically in receptors associated with infection-related CVDs. This matrix is essential for understanding the nuances of molecular interactions that govern the stability and affinity of AMP binding to these receptors. The correlation coefficients, ranging from − 1 to 1, indicate the strength and direction of the relationships between binding energy and individual energy components. Positive values suggest a direct relationship, while negative values indicate an inverse relationship, offering valuable insights into the binding mechanisms of these AMP-receptor complexes. In the ACE2 complexes, a moderate positive correlation (r = 0.68) between binding energy and van der Waals energy suggests that van der Waals interactions significantly stabilize these complexes. This implies that the physical interactions between the AMP and the ACE2 receptor are primarily driven by non-covalent van der Waals forces, contributing to a strong binding affinity. In contrast, electrostatic energy (r = 0.12) and desolvation energy (r = 0.31) show much weaker correlations, indicating that these energy components have a minimal impact on the overall binding energy in ACE2 complexes. The dominance of van der Waals interactions in these complexes suggests that designing AMP-based therapies targeting ACE2 receptors for preventing infection-related CVDs should prioritize optimizing hydrophobic and steric interactions to enhance binding stability. For the C-reactive protein (CRP) complexes, van der Waals energy (r = 0.61) also shows a significant positive correlation, reinforcing the importance of these interactions in maintaining strong AMP-receptor binding. However, electrostatic energy exhibits a negative correlation (r = -0.27), suggesting that unfavorable electrostatic interactions may slightly weaken the binding affinity. The relatively modest positive correlation for desolvation energy (r = 0.29) indicates that solvation effects do not play a significant role in these complexes. The results imply that, while van der Waals forces are crucial, electrostatic repulsion may limit the binding efficiency of AMPs to CRP receptors. In the case of MMP9 receptor complexes, van der Waals energy (r = 0.39) shows a weaker correlation with binding energy than the other receptors, suggesting a reduced contribution of hydrophobic interactions to the binding affinity. Both electrostatic energy (r = -0.13) and desolvation energy (r = 0.019) display near-zero correlations, indicating that these forces have a negligible impact on binding stability. This suggests that, for MMP9 complexes, neither van der Waals nor electrostatic interactions are particularly dominant. This may point to other factors, such as peptide conformation or flexibility, playing a more prominent role in binding affinity. NLRP3 complexes present a more balanced interaction profile, with van der Waals energy (r = 0.44) and electrostatic energy (r = 0.32) showing moderate positive correlations. In contrast, desolvation energy (r = -0.39) exhibits a strong negative correlation. This indicates that while van der Waals and electrostatic interactions contribute to binding stability, desolvation effects may destabilize these complexes. The negative impact of desolvation energy could arise from the displacement of water molecules around the receptor site, destabilizing the AMP-receptor complex. Optimizing AMPs for NLRP3 could involve minimizing the unfavorable desolvation contributions while enhancing van der Waals and electrostatic interactions. Lastly, the TLR4 complexes reveal a strong positive correlation between binding energy and van der Waals energy (r = 0.61), similar to the ACE2 and CRP complexes. This suggests that van der Waals forces are again crucial in stabilizing the AMP-TLR4 complexes. However, electrostatic energy (r = -0.032) shows a near-zero correlation, indicating minimal electrostatic contributions to the overall binding energy. The desolvation energy (r = 0.15) exhibits a weak positive correlation, suggesting that solvation effects play a relatively minor role in these complexes. Table 2 Intermolecular contacts and non-interacting surface areas for receptors associated with infection-related CVD complexes with standard inhibitor and antimicrobial peptides. This table highlights the specific interactions and spatial characteristics between the receptors and both the standard inhibitor (DX600 peptide) and the selected antimicrobial peptides (AMPs), aiding in the evaluation of their binding efficacy and potential therapeutic applications. Complex ICs charged-charged ICs charged-polar ICs charged-apolar ICs polar-polar ICs polar-apolar ICs apolar-apolar NIS charged NIS apolar ACE2 Complexes ACE2:DX600 peptide (standard inhibitor) 3 3 14 0 5 6 27.88 33.63 ACE2:Hepcidin 12 3 28 0 7 7 27.54 33.86 ACE2:Pleurocidin 8 5 23 0 11 4 27.87 34.61 ACE2:Tachystatin 5 9 20 2 12 8 26.88 33.76 ACE2:Thermolysin 4 4 29 2 9 4 27.33 34.11 ACE2:PvHCt 14 3 16 2 7 2 27.35 34.30 CRP Complexes CRP:DX600 peptide (standard inhibitor) 4 7 16 2 15 7 26.83 40.24 CRP:Nisin 1 3 27 1 26 19 24.71 42.35 CRP:Subtilisin A 5 4 29 4 23 14 25.9 43.98 CRP:Protegrin-1 9 11 19 0 18 11 29.03 40.00 CRP:Pardaxin 2 10 24 3 21 18 25.29 44.12 CRP:Magainin 8 5 20 2 18 10 28.12 42.50 MMP9 Complexes MMP9:DX600 peptide (standard inhibitor) 6 2 26 0 7 30 23.08 43.36 MMP9:Tachystatin 4 10 22 0 20 28 22.29 41.40 MMP9:Thermolysin 6 3 29 0 11 33 24.36 42.31 MMP9:Beta-defensin 2 11 3 28 0 10 24 23.33 47.33 MMP9:Exendin-4 1 4 19 0 16 22 26.06 43.66 MMP9:Pleurocidin 12 0 27 1 8 34 22.3 46.04 NLRP3 Complexes NLRP3:DX600 peptide (standard inhibitor) 5 7 23 2 15 8 23.84 42.36 NLRP3:Subtilisin A 1 10 37 2 19 23 23.74 44.13 NLRP3:Dermcidin 14 8 28 0 15 14 25.24 42.86 NLRP3:Tachystatin 8 11 22 5 15 11 23.99 41.14 NLRP3:Thermolysin 6 11 31 0 8 4 24 41.93 NLRP3:Pleurocidin 13 6 31 1 10 20 23.97 42.84 TLR4 Complexes TLR4:DX600 peptide (standard inhibitor) 4 6 15 0 23 8 24.43 30.77 TLR4:Tachystatin 5 10 24 4 26 10 23.90 31.58 TLR4:Dermcidin 10 2 26 1 21 16 25.49 33.26 TLR4:Subtilisin A 4 5 16 2 24 28 23.57 32.82 TLR4:Nisin 6 9 20 7 25 14 23.87 31.98 TLR4:Chim2 6 8 16 1 21 14 25.34 31.51 Note: • ICs: Number of intermolecular contacts • NIS: Non-interacting surface The intermolecular contact (IC) and non-interacting surface (NIS) data in Table 2 provides a detailed assessment of the molecular interactions between receptors associated with infection-related CVDs and various AMPs, compared to the standard inhibitor DX600. This analysis highlights the unique interaction profiles of several top-performing AMPs, including Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A, across different receptors such as ACE2, CRP, MMP9, NLRP3, and TLR4. These AMPs exhibit consistent binding performance, demonstrated through favorable intermolecular contacts across various energy categories and receptor sites, positioning them as potential inhibitors for preventing infection-related CVDs. For the ACE2 receptor, Pleurocidin, Tachystatin, and Thermolysin demonstrate higher charged-apolar and polar-apolar interactions than standard inhibitor DX600. Pleurocidin, with 23 charged-apolar and 11 polar-apolar contacts, indicates strong hydrophobic and polar interactions, suggesting a robust binding stability with ACE2. Tachystatin also presents significant charged-polar (9) and polar-apolar (12) interactions, enhancing its ability to form stable complexes. Thermolysin, with the highest charged-apolar interactions (29), further highlights its potential to form energetically favorable contacts with the receptor. These enhanced interactions, alongside relatively stable NIS values for all three AMPs, suggest that Pleurocidin, Tachystatin, and Thermolysin are strong candidates for inhibiting ACE2-mediated infection pathways in CVDs. Subtilisin A and Pleurocidin stand out in CRP receptor complexes with substantial intermolecular contacts. Subtilisin A exhibits 29 charged-apolar, 23 polar-apolar, and 14 apolar-apolar interactions, outperforming the standard DX600 in every category. This strong interaction profile and a high NIS apolar value (43.98) suggest that Subtilisin A can efficiently block CRP's role in infection processes linked to CVDs. Pleurocidin also shows considerable interaction strengths, with 24 charged-apolar and 18 polar-apolar contacts, making it a competitive AMP in the CRP complex. Both peptides demonstrate high binding affinity, which could disrupt CRP's function in inflammatory responses associated with CVDs. For the MMP9 receptor, Thermolysin and Pleurocidin again show superior performance. Thermolysin, with 29 charged-apolar and 11 polar-apolar interactions, demonstrates a clear advantage in forming hydrophobic interactions crucial for MMP9 inhibition. Pleurocidin, with 27 charged-apolar interactions, also shows strong binding potential, supported by this complex's highest apolar-apolar contact count (34). These AMPs outperform the standard inhibitor DX600, which has only 26 charged-apolar contacts, suggesting that Thermolysin and Pleurocidin can better interfere with MMP9's role in infection-related tissue damage in CVDs. In the NLRP3 receptor, Tachystatin, Thermolysin, and Pleurocidin again exhibit consistently strong binding characteristics. Tachystatin displays many polar-polar (5) and apolar-apolar (11) contacts, reinforcing its stability within the NLRP3 complex. Thermolysin, with 31 charged-apolar and 8 polar-apolar interactions, showcases its significant hydrophobic interaction profile, while Pleurocidin leads with the highest charged-apolar contact count (31), emphasizing its binding efficiency. These interaction profiles indicate that these AMPs can effectively inhibit NLRP3, potentially reducing its involvement in inflammatory responses during infection-related CVDs. Finally, in TLR4 receptor complexes, Tachystatin, Subtilisin A, and Pleurocidin demonstrate strong intermolecular contacts. Tachystatin, with 24 charged-apolar and 26 polar-apolar contacts, highlights its capacity to engage with both charged and polar regions of the receptor. With 28 apolar-apolar interactions, Subtilisin A presents a solid hydrophobic binding potential. In contrast, Pleurocidin’s interaction profile, including 20 charged-apolar and 25 polar-apolar contacts, shows its versatility in forming intermolecular bonds. These interactions and comparable NIS values suggest that these AMPs can effectively inhibit TLR4-mediated infection pathways, often linked to inflammation and cardiovascular complications. Detailed molecular interaction results are available in Supplementary Data 2. Table 3 Detailed examination of hydrogen bond interactions between receptors associated with infection-related CVD complexes with standard inhibitor and antimicrobial peptides. Complex Residue (Receptor) Protein Atom (Receptor) Residue (Interacting Peptide) Protein Atom (Interacting Peptide) Interaction Distance (Å) ACE2:Hepcidin Ser19 OG Lys24 NZ 2.81 Glu23 OE2 Lys24 NZ 2.58 Asp30 OD1 Arg16 NH1 2.71 Asp30 OD2 Arg16 NH2 2.59 Asp38 OD2 Lys18 NZ 2.61 CRP:Nisin Asn61 OD1 Cys19 SG 2.94 Glu147 OE1 Lys22 NZ 2.66 Glu147 OE2 Asn20 ND2 2.93 Gln150 NE2 Gly18 O 2.97 Gln150 NE2 Cys19 O 2.86 MMP9:Tachystatin Glu111 OE2 Leu6 N 2.66 Tyr179 OH Arg3 NE 2.90 Pro180 O Thr20 OG1 2.64 Asp182 O Arg14 NH2 2.79 Gly183 O Arg14 NH1 2.88 Asp185 O Arg14 NH1 2.81 Leu188 N Tyr38 OH 2.85 Gln199 OE1 Arg3 NH1 3.11 Gln199 OE1 Arg3 NH2 2.93 Tyr393 OH Thr37 OG1 2.89 His411 O Arg40 NH2 2.86 His411 ND1 Asn10 N 3.27 Ser412 O Arg40 NH1 3.28 Ser412 O Arg40 NH2 2.75 NLRP3:Subtilisin A Gln147 OE1 Lys2 NZ 2.70 Glu150 OE2 Ala5 N 2.95 Glu150 OE2 Thr6 N 3.17 Glu150 OE2 Cys7 SG 2.95 Lys164 O Trp34 NE1 2.77 Glu425 OE2 Cys13 N 2.68 Arg452 NE Glu23 OE2 2.61 Arg452 NH2 Glu23 OE1 2.61 Arg502 NH1 Thr6 O 2.78 TLR4:Tachystatin Asn383 O Arg30 NH1 3.27 Asn383 O Arg30 NH2 2.94 Ser386 O Tyr44 OH 2.78 Lys435 NZ Cys23 O 2.74 Lys435 NZ Cys24 O 2.94 Lys435 NZ Leu27 O 2.71 His458 NE2 Val12 O 2.69 Arg460 NH2 Gly17 O 2.73 Table 3 presents a detailed examination of hydrogen bond interactions between infection-related CVD receptor complexes and AMPs compared with the standard inhibitor. For the ACE2 complex, strong interactions were observed, with the shortest hydrogen bond distance being 2.58 Å between Glu23 of ACE2 and Lys24 of Hepcidin. In the CRP complex, significant interactions include the bond between Glu147 of CRP and Lys22 of Nisin at a distance of 2.66 Å. MMP9 shows multiple hydrogen bonds, with a prominent bond between Glu111 of MMP9 and Leu6 of Tachystatin at 2.66 Å, indicating stable interaction. Similarly, the NLRP3 A complex reveals strong binding with the shortest bond between Arg452 of NLRP3 and Glu23 of Subtilisin A, both at 2.61 Å. Finally, TLR4 interactions show consistent hydrogen bonding, particularly between His458 of TLR4 and Val12 of Tachystatin at 2.69 Å, contributing to the peptide’s binding efficacy. These hydrogen bonds indicate that the antimicrobial peptides, particularly Tachystatin and Subtilisin A, form strong and stable interactions with their respective receptors, comparable to or exceeding the standard inhibitor. Molecular Dynamics (MD) Simulations Table 4 overviews the time-averaged structural properties obtained from molecular dynamics (MD) simulations of target receptor-AMP complexes. The data reveal that Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A exhibit significant structural stability and binding characteristics compared to the standard inhibitor, DX600 peptide, across various receptors associated with infection-related CVDs. For ACE2 complexes, Tachystatin shows a higher average RMSD (3.327 Å) and average RMSF (0.747 Å) compared to the standard inhibitor DX600 peptide (RMSD: 3.559 Å, RMSF: 0.792 Å), indicating a slight increase in conformational fluctuations and structural deviation. However, Tachystatin has the highest average number of hydrogen bonds (53) and the most favorable potential energy (-660,317.634 kcal/mol), suggesting that it forms more stable and energetically favorable interactions with ACE2 than DX600 peptide. Table 4 Time-averaged structural properties obtained from the MD simulations of target receptor-AMP complexes. Complex Average RMSD (Å) Average RMSF (Å) Average RoG (Å) Number of Hydrogen Bonds Between the Two Proteins Potential Energy (kcal/mol) ACE2 Complexes ACE2 (apo-protein) 2.870 0.772 2.501 N/A -440,543.758 ACE2:DX600 peptide (standard inhibitor) 3.559 0.792 2.597 50 -583,916.418 ACE2:Hepcidin 3.245 0.822 2.548 49 -477,259.112 ACE2:Pleurocidin 3.218 0.791 2.572 50 -466,473.033 ACE2:Tachystatin 3.327 0.747 2.658 53 -660,317.634 ACE2:Thermolysin 3.340 0.789 2.703 54 -572,443.081 ACE2:PvHCt 3.493 0.795 2.614 51 -603,748.294 CRP Complexes CRP (apo-protein) 2.010 0.726 1.608 N/A -136,101.530 CRP:DX600 peptide (standard inhibitor) 2.402 0.958 1.735 14 -184,719.286 CRP:Nisin 2.313 0.787 1.737 16 -288,567.674 CRP:Subtilisin A 1.977 0.757 1.704 16 -166,979.914 CRP:Protegrin-1 2.416 0.951 1.653 17 -179,352.210 CRP:Pardaxin 2.355 0.864 1.687 16 -143,351.144 CRP:Magainin 2.192 0.814 1.688 17 -145,486.514 MMP9 Complexes MMP9 (apo-protein) 2.078 1.094 1.495 N/A -111,366.323 MMP9:DX600 peptide (standard inhibitor) 2.555 1.267 1.610 11 -164,237.839 MMP9:Tachystatin 2.209 1.359 1.631 13 -195,363.451 MMP9:Thermolysin 1.992 1.295 1.666 15 -131,816.946 MMP9:Beta-defensin 2 2.498 1.330 1.641 13 -163,272.695 MMP9:Exendin-4 2.820 1.461 1.661 12 -191,827.586 MMP9:Pleurocidin 2.274 1.095 1.573 11 -119,726.773 NLRP3 Complexes NLRP3 (apo-protein) 3.176 0.924 3.614 N/A -900,750.476 NLRP3:DX600 peptide (standard inhibitor) 3.636 1.056 3.652 45 -1,127,743.577 NLRP3:Subtilisin A 3.621 1.044 3.667 48 -1,070,921.883 NLRP3:Dermcidin 3.501 1.048 3.728 50 -1,370,475.374 NLRP3:Tachystatin 3.459 0.972 3.678 49 -1,467,888.897 NLRP3:Thermolysin 3.362 0.941 3.638 53 -1,239,438.186 NLRP3:Pleurocidin 3.207 1.051 3.587 49 -977,382.530 TLR4 Complexes TLR4 (apo-protein) 2.642 0.720 3.201 N/A -718,765.881 TLR4:DX600 peptide (standard inhibitor) 2.968 0.775 3.268 38 -755,432.195 TLR4:Tachystatin 2.914 0.878 3.221 46 -780,309.925 TLR4:Dermcidin 2.938 0.900 3.234 43 -708,531.477 TLR4:Subtilisin A 3.106 0.831 3.194 45 -711,005.102 TLR4:Nisin 2.962 0.815 3.193 44 -712,561.934 TLR4:Chim2 3.143 0.918 3.220 45 -743,878.601 In CRP complexes, Subtilisin A and Pleurocidin display better structural stability with average RMSD values of 1.977 Å and 2.313 Å, respectively, compared to the standard inhibitor DX600 peptide (2.402 Å). Subtilisin A also has a comparable number of hydrogen bonds (16) and lower potential energy (-166,979.914 kcal/mol) than DX600 peptide (-184,719.286 kcal/mol), indicating efficient binding and favorable energetics. Pleurocidin exhibits a similar number of hydrogen bonds (16) and lower potential energy (-143,351.144 kcal/mol) than the DX600 peptide. For MMP9 complexes, Tachystatin and Thermolysin demonstrate lower average RMSD values (2.209 Å and 1.992 Å, respectively) compared to the standard inhibitor DX600 peptide (2.555 Å), indicating better structural stability. Tachystatin has a higher average number of hydrogen bonds (13) and more favorable potential energy (-195,363.451 kcal/mol) compared to DX600 peptide (-164,237.839 kcal/mol), suggesting that Tachystatin provides more stable and energetically favorable interactions with MMP9. In NLRP3 complexes, Tachystatin and Subtilisin A exhibit better structural stability with average RMSD values of 3.459 Å and 3.621 Å, respectively, compared to the standard inhibitor DX600 peptide (3.636 Å). Tachystatin also shows a higher number of hydrogen bonds (49) and the most favorable potential energy (-1,467,888.897 kcal/mol) among the peptides tested, indicating that it forms highly stable and energetically favorable interactions with NLRP3 compared to DX600 peptide. Finally, in TLR4 complexes, Tachystatin and Subtilisin A have lower average RMSD values (2.914 Å and 3.106 Å) compared to the standard inhibitor DX600 peptide (2.968 Å). Tachystatin also exhibits a higher number of hydrogen bonds (46) and more favorable potential energy (-780,309.925 kcal/mol) than DX600 peptide (-755,432.195 kcal/mol), suggesting that Tachystatin provides a more stable and energetically favorable binding interaction with TLR4. RMSF values offer a detailed view of residue flexibility within receptors associated with infection-related CVD-AMP complexes, as depicted in Fig. 4 . The results indicate that the AMPs exhibit a significant correspondence with the standard inhibitor, DX600 peptide, regarding residue flexibility, suggesting that these AMPs can disrupt receptor stability similarly to the standard inhibitor. The ACE2 complexes' RMSF patterns of Pleurocidin, Tachystatin, and Thermolysin closely resemble those of the standard inhibitor, particularly within residues Asn330 to Asp355. In CRP complexes, the RMSF values of these AMPs show a strong correlation with the standard inhibitor around residues Ala55 to Ile65 and Glu130 to Asp155. For MMP9 complexes, the RMSF profiles of Pleurocidin, Tachystatin, and Thermolysin match those of the standard inhibitor in residues Ile125 to Asp138. In the NLRP3 and TLR4 complexes, the RMSF values of the AMPs align closely with those of the standard inhibitor in crucial binding regions, including residues Ser161 to His175 and Lys375 to Asn400 for NLRP3, and Ser360 to Leu380 and Gln510 to Leu535 for TLR4. Overall, the RMSF data highlight that the AMPs can disrupt receptor stability in a manner similar to the standard inhibitor. The ability of these AMPs to induce comparable flexibility in critical binding regions underscores their potential as effective disruptors of receptor stability, akin to the DX600 peptide. Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) Calculations The binding affinities of selected AMPs for target receptors were assessed using MM/PBSA calculations (based on the MD simulation), with results in Table 5 . Among the peptides evaluated, Tachystatin, Pleurocidin, and Subtilisin A emerged as the most consistent in exhibiting favorable binding energies. For ACE2 complexes, Tachystatin stands out with an average binding energy of -61.58 kcal/mol, significantly more favorable than the standard inhibitor DX600 peptide, which has an average binding energy of -22.28 kcal/mol. Pleurocidin also shows strong binding with an average energy of -46.58 kcal/mol, while Subtilisin A’s binding affinity is slightly less favorable at -44.82 kcal/mol. These results suggest that Tachystatin and Pleurocidin exhibit superior binding capabilities compared to the standard inhibitor, with Tachystatin showing the most significant potential. In CRP complexes, Subtilisin A exhibits the most favorable binding energy with an average of -70.71 kcal/mol, followed by Protegrin-1 at -67.56 kcal/mol and Nisin at -38.73 kcal/mol. This contrasts with the standard inhibitor DX600 peptide, which has an average binding energy of -27.99 kcal/mol. The superior binding energy of Subtilisin A and Protegrin-1 in CRP complexes underscores their effectiveness compared to the standard inhibitor. Table 5 Time-averaged structural properties obtained from the MD simulations of target receptor-AMP complexes. Complex MM/PBSA Calculation Results ΔG binding (kcal/mol) Average (kcal/mol) I II III ACE2 Complexes ACE2:DX600 peptide (standard inhibitor) -22.07 -22.51 -22.27 -22.28 ACE2:Hepcidin -53.81 -53.35 -52.56 -53.24 ACE2:Pleurocidin -46.94 -46.17 -46.64 -46.58 ACE2:Tachystatin -62.34 -60.47 -61.93 -61.58 ACE2:Thermolysin -44.14 -45.63 -44.70 -44.82 ACE2:PvHCt -31.42 -31.08 -31.18 -31.22 CRP Complexes CRP:DX600 peptide (standard inhibitor) -28.23 -27.82 -27.93 -27.99 CRP:Nisin -38.81 -38.65 -38.75 -38.73 CRP:Subtilisin A -70.24 -70.99 -70.92 -70.71 CRP:Protegrin-1 -67.52 -67.86 -67.31 -67.56 CRP:Pardaxin -60.72 -56.91 -60.72 -59.45 CRP:Magainin -53.41 -53.47 -53.35 -53.41 MMP9 Complexes MMP9:DX600 peptide (standard inhibitor) -53.14 -53.07 -51.63 -52.61 MMP9:Tachystatin -96.59 -96.7 -96.55 -96.61 MMP9:Thermolysin -66.02 -68.04 -65.97 -66.67 MMP9:Beta-defensin 2 -78.69 -78.72 -77.75 -78.38 MMP9:Exendin-4 -23.95 -23.51 -23.60 -23.68 MMP9:Pleurocidin -94.82 -94.15 -93.77 -94.24 NLRP3 Complexes NLRP3:DX600 peptide (standard inhibitor) -43.87 -45.63 -43.57 -44.35 NLRP3:Subtilisin A -69.45 -71.10 -72.81 -71.12 NLRP3:Dermcidin -61.90 -62.54 -62.43 -62.29 NLRP3:Tachystatin -69.04 -69.56 -69.02 -69.20 NLRP3:Thermolysin -28.14 -28.87 -28.77 -28.59 NLRP3:Pleurocidin -60.13 -55.24 -60.83 -58.73 TLR4 Complexes TLR4:DX600 peptide (standard inhibitor) -33.05 -32.48 -32.93 -32.82 TLR4:Tachystatin -59.73 -59.72 -59.62 -59.69 TLR4:Dermcidin -45.92 -46.23 -44.51 -45.55 TLR4:Subtilisin A -43.62 -42.51 -43.61 -43.24 TLR4:Nisin -56.90 -57.13 -56.90 -56.97 TLR4:Chim2 -57.95 -58.19 -58.45 -58.19 For MMP9 complexes, Tachystatin again demonstrates the highest binding affinity with an average energy of -96.61 kcal/mol, followed closely by Pleurocidin at -94.24 kcal/mol. These values are significantly lower (more favorable) than the standard inhibitor DX600 peptide, which has an average energy of -52.61 kcal/mol. The binding energies of Tachystatin and Pleurocidin indicate their strong interaction with MMP9, surpassing that of the standard inhibitor. In NLRP3 complexes, Subtilisin A and Tachystatin exhibit comparable binding affinities with averages of -71.12 kcal/mol and − 69.20 kcal/mol, respectively, outperforming the standard inhibitor DX600 peptide, which has an average of -44.35 kcal/mol. This highlights the superior binding potential of Subtilisin A and Tachystatin for NLRP3. Finally, in TLR4 complexes, Tachystatin displays a favorable binding energy of -59.69 kcal/mol, more favorable than the standard inhibitor DX600 peptide, with an average energy of -32.82 kcal/mol. Subtilisin A’s average binding energy is -43.24 kcal/mol, indicating that it also binds effectively, though less so than Tachystatin. Haemolytic Activity Prediction of Antimicrobial Peptides (AMPs) The DX600 peptide, which serves as the standard inhibitor in this study, exhibited a very low PROB score of 0.004, indicating a strong likelihood of being non-hemolytic. This suggests that DX600 may be a safer therapeutic candidate when considering the potential for hemolysis, particularly in clinical settings. In contrast, several peptides, such as Beta-defensin 2 (PROB = 0.967), Chim2 (PROB = 0.973), and Pardaxin (PROB = 0.988), showed significantly higher PROB scores, indicating a substantial risk of hemolytic activity. This finding raises important considerations for their therapeutic application, as hemolytic peptides may lead to adverse effects in vivo, potentially limiting their clinical utility. Among the AMPs evaluated, Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A emerged as the most promising candidates based on their molecular docking and MD simulations, alongside their relatively low PROB scores, indicating a lower risk of hemolysis (Table 6 ). Tachystatin demonstrated a PROB score of 0.576, indicating a moderate hemolytic activity risk. Thermolysin exhibited a PROB score of 0.288, suggesting a lower hemolytic activity likelihood than many other peptides. Pleurocidin, with a PROB score of 0.095, displayed the lowest potential for hemolysis among the evaluated peptides. Subtilisin A, with a PROB score of 0.049, also showed a very low likelihood of hemolytic activity. Their favorable interaction profiles and stability in complexes with target receptors enhance their appeal as therapeutic candidates. Table 6 Haemolytic activity prediction of selected AMPs. The table presents the antimicrobial peptides' sequences and their corresponding PROB scores, which indicate their likelihood of causing haemolysis; lower scores suggest a reduced potential for toxicity. Antimicrobial Peptide Sequence nTer cTer PROB DX600 peptide (standard inhibitor) GDYSHCSPLRYYPWWKCTYPDPEGGG None None 0.004 Aurein GLFDIIKKIAESF None None 0.779 Beta-defensin 2 MRVLYLLFSFLFIFLMPLPGVFGGIGDPVTCLKSGAICHPVFCPRRYKQIGTCGLPGTKCCKKP None None 0.967 Bombinin IIGPVLGLVGSALGGLLKKIG None None 0.742 Cathelicidin LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES None None 0.258 Cecropin KWKLFKKIGKFLHSAKKF None None 0.050 Chim2 KWAVKIIRKFIKGFISGGKRWKYM None None 0.973 Dermcidin SSLLEKGLDGAKKAVGGLGKLGKDAVEDLESVGKGAVHDVKDVLDSVL None None 0.001 Esculentin GIFSKLAGKKIKNLLISGLKNVGKEVGMDVVRTGIDIAGCKIKGEC None None 0.166 Exendin-4 DLSKQMEEEAVRLFIEWLKNGGPSSGA None None 0.007 Hepcidin HFPICIFCCGCCHRSKCGMCCK None None 0.026 Hs05 LMGLFNRIIRKVVKLFN None None 0.961 Indolicidin ILAWKWAWWAWRR None None 0.958 Lactoferrin FKCRRWQWRMKKLGAPSITCVRRAF None None 0.001 Lavracin WDPYFAGVKKLTKAILAVRA None None 0.107 Magainin GIGKFLHSAKKFGKAFVGEIMNS None None 0.820 Melittin MKFLVNVALVFMVVYISYIYAAPEPEPAPEPEAEADAEADPEAGIGAVLKVLTTGLPALISWIKRKRQQG None None 0.450 Microcin J25 GGAGHVPEYFVGIGTPISFYG None None 0.002 Nisin MSTKDFNLDLVSVSKKDSGASPRITSISLCTPGCKTGALMGCNMKTATCNCSIHVSK None None 0.006 Pardaxin GFFALIPKIISSPLFKTLLSAVGSALSSSGGQE None None 0.988 Piscidin FIHHIFRGIVHAGRSIGRFLTG None None 0.343 Pleurocidin GWGSFFKKAAHVGKHVGKAALTHYL None None 0.095 Polyphemusin I RRWCFRVCYRGFCYRKCR None None 0.867 Protegrin-1 RGGRLCYCRRRFCVCVGR None None 0.911 PvHCt FQDLPNFGHIQVKVFNHGEHIHH None None 0.272 Subtilisin A NKGCATCSIGAACLVDGPIPDFEIAGATGLFGLWG None None 0.049 Tachyplesin-1 KWCFRVCYRGICYRRCR None None 0.663 Tachystatin MKLQNTLILIGCLFLMGAMIGDAYSRCQLQGFNCVVRSYGLPTIPCCRGLTCRSYFPGSTYGRCQRY None None 0.576 Temporin-L FVQWFSKFLGRIL None None 0.993 Thanatin GSKKPVPIIYCNRRTGKCQRM None None 0.001 Thermolysin GIGRDKLGKIFYRALTQYLTPTSNFSQLRAAAVQSATDLYGSTSQEVASVKQAFDAVGV None None 0.288 Note : The PROB score is a normalized sigmoid value that ranges from 0 to 1. A score of 0 indicates a high likelihood of being non-hemolytic, while a score of 1 suggests a high likelihood of being hemolytic. nTer is the N-terminal of a peptide, indicating the beginning of the peptide chain, which has a free amino group (-NH₂). cTer is the C-terminal of a peptide, indicating the end of the peptide chain, which has a free carboxyl group (-COOH). Discussion Key Findings from Molecular Simulations and Their Correlation with Other Research The molecular docking and dynamics simulations conducted in this study provided significant insights into the interactions between AMPs and receptors implicated in infection-related CVDs. These findings highlight the potential of AMPs as therapeutic agents and contribute to our understanding of their binding mechanisms and stability compared to conventional inhibitors. Our simulations highlight that several AMPs—Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A—demonstrate significant binding affinity towards key receptors such as ACE2, MMP9, CRP, NLRP3, and TLR4. These peptides exhibited strong binding interactions, as indicated by favorable HADDOCK scores and binding energies. Notably, Tachystatin and Thermolysin showed particularly strong binding with ACE2, and Pleurocidin performed well with multiple receptors, including ACE2 and MMP9. These findings are consistent with previous studies that have demonstrated the potential of AMPs to modulate receptor activity in various disease contexts 30 , 31 . In relation to prior research, Tachystatin's superior binding affinity for ACE2 aligns with a finding that peptides targeting ACE2 could modulate its activity and influence cardiovascular disease outcomes 32 . Similarly, the strong binding of Thermolysin and Pleurocidin to MMP9 and CRP reflects their potential to target inflammation-related pathways. These corroborating studies emphasize the role of peptides in controlling matrix metalloproteinase activity and inflammatory responses 16 , 33 , 34 . MD simulations provided insights into the stability and conformational dynamics of AMP-receptor complexes. Tachystatin and Pleurocidin, in particular, displayed favorable structural stability with lower RMSD and RMSF values compared to the standard inhibitor DX600. These observations are supported by previous studies showing the importance of peptide stability in maintaining therapeutic efficacy 35 – 37 . The consistent hydrogen bonding patterns and favorable potential energies observed in our simulations underscore the potential of these AMPs to form stable and energetically favorable interactions with their targets. Furthermore, our results suggest that van der Waals interactions are crucial for binding stability, as corroborated by studies that emphasize the role of non-covalent interactions in peptide-receptor binding 38 , 39 . Our molecular simulations provide valuable insights into the binding mechanisms and stability of AMPs interacting with infection-related CVD receptors. The results validate previous research on peptide-receptor interactions and highlight the potential of these AMPs as therapeutic candidates for modulating infection-related pathways in CVDs. Moreover, we conducted a toxicity assessment through haemolytic activity prediction using the HAPPENN tool, which leverages advanced neural network algorithms to predict the potential for hemolytic activity in therapeutic peptides. This analysis provided valuable insights into the safety profiles of the evaluated AMPs, which are crucial for their development as therapeutic agents. Hemolysis, the rupture of red blood cells, can lead to serious side effects and is a significant concern when considering the therapeutic application of peptides 40 . Our findings reveal variability in the hemolytic activity among the tested peptides, with some exhibiting a favorable safety profile while others raised concerns regarding their potential toxicity. For instance, certain standard inhibitors demonstrated low hemolytic activity. Such variations underscore the complexity of peptide interactions with host cells and highlight the importance of thorough toxicity assessments in the early stages of drug development 41 . This observation is consistent with existing literature, which indicates that the toxicity of AMPs can vary significantly based on their structure and composition. For example, several studies have reported that modifications in the amino acid sequence, charge distribution, and hydrophobicity of AMPs can dramatically influence their hemolytic activity and overall safety 42 , 43 . Research has shown that AMPs with a high proportion of cationic residues tend to have increased hemolytic effects due to their ability to disrupt membrane integrity 44 , 45 . Conversely, modifications to reduce the cationic charge can lead to decreased hemolytic activity without compromising antimicrobial efficacy, providing a dual advantage in therapeutic applications 46 . In addition, the potential for AMPs to harm host cells at elevated concentrations has been widely documented 47 . This has prompted researchers to explore strategies for optimizing peptide design to enhance therapeutic efficacy while minimizing cytotoxic effects. For instance, some studies have focused on developing peptide analogues with reduced cytotoxicity through structure-activity relationship (SAR) analyses, enabling the identification of variants with improved safety profiles 48 , 49 . Clinical Implications and Limitations Exploring peptide-receptor interactions has significant clinical implications, particularly in developing targeted therapies and precision medicine. Understanding the fundamental mechanisms by which peptides bind to their receptors provides crucial insights for designing novel therapeutics that target specific biological pathways more effectively. For instance, inhibiting MMPs by peptides has been shown to play a critical role in managing cardiovascular diseases by preventing the degradation of extracellular matrix components, thereby improving vascular stability and function 34 , 50 . Similarly, peptides that target ACE2 could potentially modulate cardiovascular disease outcomes, offering new avenues for treatment 32 . These findings underscore the importance of peptide-receptor interactions in crafting targeted therapeutic interventions. However, several limitations associated with peptide-based therapies must be addressed. One primary challenge is the stability of peptides in physiological conditions, which often impacts their efficacy. Peptides are susceptible to rapid degradation by proteolytic enzymes, reducing their therapeutic potential and necessitating the development of strategies to enhance their stability 51 , 52 . For instance, many peptides can be hydrolyzed by enzymes like trypsin and chymotrypsin, which are abundant in the digestive system 53 . This enzymatic action can result in the loss of the peptides' bioactive properties before they reach their intended target sites. Moreover, the rapid turnover and clearance of peptides from the systemic circulation can limit their effective concentration at the site of action, necessitating frequent dosing to achieve desired therapeutic outcomes 54 . Therefore, it is crucial to develop strategies to enhance peptide stability and resistance to enzymatic degradation. Such strategies may include chemical modifications, such as incorporating non-proteogenic amino acids or using cyclization to create more stable structures 55 , 56 . Additionally, the specificity of peptide-receptor interactions is crucial for minimizing off-target effects and maximizing therapeutic outcomes 57 . While advances in computational modeling and simulations have improved our understanding of these interactions, translating these findings into clinical applications remains challenging 58 . Another limitation is the potential for adverse immune responses to peptide-based therapies. Peptides can sometimes trigger immune reactions, leading to reduced patient efficacy or adverse effects 59 , 60 . Furthermore, the cost and complexity of developing and producing peptide-based drugs can be significant, potentially limiting their accessibility and widespread use 61 , 62 . Therefore, while peptide-based therapeutics offer promising prospects, ongoing research and development are needed to address these limitations and optimize their clinical application. Conclusions In conclusion, the molecular docking and dynamics simulations provide compelling insights into the interactions of AMPs with receptors implicated in infection-related CVDs. Our simulations indicated that Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A exhibited favorable binding affinities and stability with key receptors such as ACE2, CRP, MMP9, NLRP3, and TLR4 compared to the standard inhibitor DX600. Tachystatin demonstrated consistent and strong binding with multiple receptors, including ACE2 and MMP9, with favorable HADDOCK scores and binding energies. Thermolysin and Pleurocidin also showed potential, exhibiting significant interaction profiles and stability across different receptor complexes. Subtilisin A, while showing robust performance with CRP and NLRP3, demonstrated impressive binding characteristics with TLR4. The detailed analysis of energy components, intermolecular contacts, and hydrogen bonds further supports the potential for these AMPs to disrupt receptor-mediated infection pathways. While the results from this in silico study highlight the promise of these AMPs, it is crucial to note that further experimental validation, including in vitro and preclinical studies, is needed to establish their therapeutic efficacy. Future research should aim to translate these findings into practical applications for preventing and treating CVDs associated with infection and inflammation. Materials and Methods Selection and Preparation of the Receptors Associated with Infection-related CVDs In this study, five key receptors associated with infection-related CVDs were selected for analysis: Angiotensin-Converting Enzyme 2 (ACE2), C-reactive protein (CRP), Matrix Metalloproteinase-9 (MMP9), NLRP3 Inflammasome, and Toll-Like Receptor-4 (TLR4) (Table 7 ). To ensure accurate molecular docking and dynamics simulations, these proteins were chosen based on their well-characterized functions and the availability of high-resolution 3D structures in the Protein Data Bank (PDB). The preparation process involved retrieving the structural data, optimizing the protein structures, and refining them using Swiss-PdbViewer 63 to correct potential issues and enhance model accuracy. This refinement process included adjusting side-chain conformations, adding missing residues, and validating structural quality, ensuring the proteins were suitable for precise interaction modeling. Additionally, their active sites were defined using CASTp 3.0 64 , facilitating detailed docking studies with the selected antimicrobial peptides. Table 7 Overview of target receptors related to infection-related cardiovascular diseases. It includes the Protein Data Bank (PDB) IDs for each receptor and details their respective active sites, which are critical for understanding potential interactions with antimicrobial peptides (AMPs). Receptors Associated with Infection-related CVDs PDB ID Active Site (Number of Residues) Ref Angiotensin-Converting Enzyme 2 (ACE2) 6M0J 24, 30, 35, 38, 41, 42, 83, 353 65 C-reactive protein (CRP) 1B09 58, 60, 61, 66, 74, 81, 138, 139, 140, 147, 150 66 Matrix Metalloproteinase-9 (MMP9) 1GKC 131, 149, 165, 175, 177, 182, 183, 185, 186, 187, 188, 189, 190, 197, 199, 201, 203, 205, 206, 208, 211 ,212, 213, 214, 215, 393, 401, 402, 405, 421, 422, 423 67 NLRP3 Inflammasome 6NPY 165, 166, 167, 229, 230, 231, 232, 379, 411, 414, 520 68 Toll-Like Receptor-4 (TLR4) 3FXI 363, 364, 365, 386, 409, 410, 411, 433, 458, 507, 533 69 Selection and Preparation of Antimicrobial Peptides (AMPs) Specific criteria were employed to guide the selection of AMPs for this study, ensuring the relevance and reliability of the molecular docking and dynamics simulations. Only peptides with available 3D structures in the PDB were considered, guaranteeing the availability of accurate structural data necessary for effective modeling and simulation. The peptides chosen were classified under the SCOP (Structural Classification of Proteins) as "peptides." The resolution of the structural data was restricted to 0.5–2.5 Å, as high-resolution structures are preferred for their detailed and accurate depiction of peptide conformation, which is crucial for precise docking simulations. The binding regions of the AMPs were analyzed using CASTp 3.0 64 . The complete dataset, including type, PDB ID, sequence, and active residues of the selected AMPs, is provided in Supplementary Data 3. Table 8 Selected antimicrobial peptides (AMPs) based on the employed criterion. It includes their respective PDB IDs, molecular sizes (kDa), and binding regions, which are essential for assessing their binding capabilities and therapeutic relevance in infection-related CVDs. Antimicrobial Peptide PDB ID Size (kDa) Binding Regions (Position of Residues) Ref Aurein 1VM5 1.48 1, 2, 5 70 Beta-defensin 2 1FD4 7.04 6, 7, 9, 10, 11, 12 71 Bombinin 2AP7 1.98 1, 3, 4, 6, 7 72 Cathelicidin 2K6O 4.49 1, 4, 5, 8 73 Cecropin 1D9J 2.23 7, 9, 10, 11, 12 74 Chim2 8EB1 2.94 10, 11, 14, 15 75 Dermcidin 2NDK 4.82 18, 19, 22, 25, 26, 29 76 Esculentin 5XDJ 4.80 2, 3, 6 77 Exendin-4 3C59 2.99 26, 27, 28, 29, 32, 33 78 Hepcidin 3H0T 2.48 13, 14, 16, 18, 19, 20, 21, 22 79 Hs05 6VLA 2.06 5, 8, 9 80 Indolicidin 1HR1 1.83 9, 10, 11, 12, 13 81 Lactoferrin 1LFC 3.13 1, 23, 25 82 Lavracin 2N8D 2.25 1, 3, 4, 6 83 Magainin 2MAG 2.47 1, 2, 6, 9, 17, 21 84 Melittin 2MLT 7.59 13, 16, 17, 20 85 Microcin J25 4CU4 2.13 9, 10, 19, 20, 21 86 Nisin 1WCO 5.94 9, 12, 17, 19, 20, 21 87 Pardaxin 2KNS 3.32 2, 3, 5, 6, 9, 15, 22, 23, 26, 27, 29, 30, 33 88 Piscidin 6PEZ 2.49 3, 4, 7 89 Pleurocidin 2LS9 2.71 10, 13, 14, 17, 20, 23, 24 90 Polyphemusin I 1RKK 2.46 7, 9, 12, 14 91 Protegrin-1 1PG1 2.16 5, 6, 7, 14, 15, 16 92 PvHCt 2N1C 2.75 14, 15, 16, 17, 18, 19, 20, 22, 23 93 Subtilisin A 1PXQ 3.43 1, 4, 5, 7, 9, 10, 24, 25, 29, 30, 33 94 Tachyplesin-1 2RTV 2.27 1, 2, 3, 16, 17 95 Tachystatin 1CIX 7.51 5, 11, 12, 15, 18, 22, 23, 29 96 Temporin-L 6GS5 1.64 4, 7 97 Thanatin 8TFV 2.44 11, 13, 16, 17, 18 98 Thermolysin 6FHP 6.30 258, 263, 267, 305, 306, 309, 310 99 Based on these criteria, a set of 30 AMPs was chosen (Table 8 ), including Exendin-4, an AMP with similarities to GLP-1, and has been investigated for its potential to influence ACE2 activity 100 . CRP is a marker of inflammation often elevated in cardiovascular diseases 101 . Lactoferrin, an iron-binding glycoprotein known for its antimicrobial properties, has been shown to affect CRP levels 102 , thus, Lactoferrin potentially influences cardiovascular outcomes and highlights its role in controlling inflammation in cardiovascular diseases. Cathelicidin, another well-studied AMP, has been shown to modulate the NLRP3 inflammasome, a critical component of the inflammatory response linked to cardiovascular diseases 103 . These examples illustrate how AMPs interact with key receptors involved in cardiovascular diseases, offering valuable insights into their potential therapeutic applications for modulating receptor activity and managing disease progression. Molecular Docking Simulations In this phase, molecular docking simulations were meticulously performed to investigate the interactions between a selected set of AMPs and specific receptors implicated in cardiovascular diseases. These receptors are critical targets in cardiovascular diseases and infections, influencing disease progression and inflammatory responses. The molecular docking simulations aimed to investigate the interactions between the selected AMPs and CVD-related receptors associated with infection and inflammation pathways. Key interaction parameters were analyzed to understand better how AMPs bind to these target receptors. The docking results included an evaluation of the HADDOCK score, free binding energy (kcal/mol), van der Waals energy, electrostatic energy, and desolvation energy for various AMP-receptor combinations. The simulations were executed using the stand-alone version of HADDOCK (High Ambiguity Driven protein-protein DOCKing) 104 , a robust and versatile docking software that enables detailed exploration of binding modes and energetic interactions between complex biomolecules. HADDOCK is well-regarded for incorporating experimental data into the docking process, allowing for a more accurate prediction of the possible binding conformations between AMPs and target receptors. The DX600 peptide (sequence: GDYSHCSPLRYYPWWKCTYPDPEGGG) was used as a reference standard due to its known interaction with CVD-related receptors like ACE2 (IC 50 : 10.1 µM) and therapeutic implications in cardiovascular conditions 105 . The 3D structure of peptide 35409 was generated using AlphaFold 106 , providing a precise model for further analysis. DX600 peptide was utilized as a benchmark to evaluate the efficacy of other AMPs in binding to the target receptors. Using this standard peptide allowed for a comparative analysis of the binding efficiency and inhibitory potential of other AMPs. The insights gained from these simulations are intended to guide the selection of AMPs with the highest potential for therapeutic applications. To further refine and validate the docking results, PRODIGY (PROtein binDIng enerGY prediction) 107 was employed to predict the binding affinity of the AMP-adhesion protein complexes. PRODIGY is an advanced computational tool that leverages state-of-the-art algorithms to estimate the binding affinity between interacting proteins and ligands based on structural data 108 . This prediction of binding free energy is critical for ranking the AMP candidates, as it provides a quantitative measure of how strongly an AMP binds to a target receptor. By identifying the AMPs with the most favorable binding affinities, PRODIGY helps narrow down the candidates most likely to impact cardiovascular disease receptors effectively. All molecular docking simulations were conducted on a high-performance computing workstation with an Intel® Core™ i7-12650H processor, an NVIDIA™ RTX 4060 graphics card with 8 GB VRAM, and 16 GB of DDR5 RAM. Molecular Dynamics (MD) Simulations Molecular dynamics (MD) simulations were utilized to explore the dynamics and stability of AMP-receptor complexes, specifically focusing on interactions between AMPs and receptors implicated in infection-related CVDs. The simulations were conducted using GROMACS 2022.5 109 , a highly regarded tool known for its accuracy and efficiency in modeling biomolecular systems. The Optimized Potentials for Liquid Simulations (OPLS-AA/L) force field was employed to accurately represent molecular interactions within these complexes 110 , 111 . Simulation boxes were configured using default cubic box parameters to accommodate the AMP-receptor complexes effectively. Standard procedures were followed, including adding water molecules via the Single Point Charge Extended (SPCE) model and incorporating counterions to ensure system neutrality 112 . Energy minimization was conducted using the steepest-descent method to eliminate steric clashes and stabilize the system. Equilibration was performed in two stages: first, in the number of particles, volume, and temperature (NVT) ensemble to stabilize temperature and system conditions, and second, in the number of particles, pressure, and temperature (NPT) ensemble to maintain constant pressure and temperature 28 , 113 . Following equilibration, production MD simulations were conducted for 100 nanoseconds to observe the long-term dynamics of the AMP-receptor complexes. During the simulations, parameters such as Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (RoG), potential energies, and intermolecular hydrogen bonding interactions were monitored and analyzed to evaluate the stability and conformational dynamics of the complexes. Molecular visualization software, including PyMOL 114 and UCSF Chimera 115 , was used to visualize critical residues and intermolecular interactions within the simulated complexes. This analysis provided valuable insights into the binding mechanisms and stability of the AMPs with the cardiovascular disease receptors. Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) Calculations The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) method evaluated the peptide-receptor interactions involving AMPs and receptors implicated in infection-related CVDs. This method utilizes MD simulation data to calculate the binding free energy of the AMP-receptor complexes, providing insights into the strength and stability of these interactions. The MD simulations generated a variety of receptor conformations, and representative snapshots from these simulations were selected for detailed analysis 116 . Comprehensive energy computations were performed for each snapshot, including gas-phase energy calculations, solvation energy estimation using a continuum solvent model, and entropy calculations. These components were integrated to determine the overall binding free energy of the AMP-receptor complex 117 , 118 . The calculations used the gmx_MMPBSA module available within the GROMACS simulation package 119 , 120 . It is well-regarded for its precision and efficiency in calculating binding free energies for biomolecular complexes. The MM/PBSA method is precious for predicting binding affinities, as it accounts for the energetic and solvation contributions to the binding process 121 . The binding free energy (ΔG_binding) was computed using the following equation: ΔG_binding = ΔG_complex - ΔG_peptide - ΔG_protein Where: ΔG_binding: the binding free energy associated with forming the peptide-protein complex. ΔG_complex: the free energy of the fully solvated peptide-protein complex. ΔG_peptide: the free energy of peptide in its solvated state when unbound. ΔG_protein: the free energy of protein in its solvated state when unbound. By calculating the difference between the free energy of the complex and the combined free energies of the unbound AMP and receptor, this method provided insights into the energetic changes that occur upon complex formation, elucidating the interaction's strength and stability. Haemolytic Activity Prediction of Antimicrobial Peptides (AMPs) The haemolytic activity of selected AMPs was evaluated using the Hemolytic Activity Prediction for Peptides and Proteins (HAPPENN) tool. HAPPENN is a novel computational tool designed specifically for predicting the hemolytic potential of therapeutic peptides by employing advanced neural network algorithms 122 . The amino acid sequences of the selected AMPs were input into the HAPPENN platform to conduct the haemolytic activity assessment. This tool utilizes a dataset of known peptides with established haemolytic activity to train its neural network. The training process involves analyzing the structural and compositional features of the peptides, such as hydrophobicity, charge distribution, and amino acid sequences, which are critical in determining their interaction with erythrocyte membranes. Once the input sequences were processed, HAPPENN generated predictions regarding the haemolytic potential of each peptide. The output includes a quantitative score that reflects the likelihood of a given AMP to induce hemolysis in red blood cells, allowing for a comparative assessment of the AMPs' safety profiles. Abbreviations ACE2 Angiotensin-converting enzyme 2 AMPs Antimicrobial peptides CRP C-reactive protein CVDs Cardiovascular diseases HAPPENN Hemolytic activity prediction for peptides and proteins IC Intermolecular contact MD Molecular dynamics MDR Multidrug-resistant MM/PBSA Molecular Mechanics/Poisson–Boltzmann Surface Area MMPs Matrix metalloproteinases NIS Non-interacting surface OPLS-AA/L Optimized potentials for liquid simulations PAMPs Pathogen-associated molecular patterns RMSD Root mean square deviation RMSF Root mean square fluctuation RoG Radius of gyration SCOP Structural classification of proteins SPCE Single point charge extended TLRs Toll-like receptors Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no conflicts of interest. Funding This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-RG23154). Author Contribution D.D. and N.A. contributed to the conception, design and supervision of the work and revision of the manuscript. D.D. contributed to the acquisition and analysis of the computational data/results. D.D. and N.A. wrote the manuscript. All authors have read and approved the final manuscript. Acknowledgements Not applicable. Data Availability Data is provided within the manuscript or supplementary information files. References Chen, L. et al. Inflammatory responses and inflammation-associated diseases in organs. Oncotarget . 9 , 7204–7218. 10.18632/oncotarget.23208 (2018). Yang, T. H. et al. A Review on The Pathogenesis of Cardiovascular Disease of Flaviviridea Viruses Infection. Viruses . 16 , 365 (2024). Liu, C. & Waters, D. D. Chlamydia pneumoniae and atherosclerosis: from Koch postulates to clinical trials. Prog Cardiovasc. Dis. 47 , 230–239. 10.1016/j.pcad.2005.01.001 (2005). Jung, S. H. & Lee, K. T. Atherosclerosis by Virus Infection-A Short Review. Biomedicines . 10 10.3390/biomedicines10102634 (2022). Wolf, D. & Ley, K. Immunity and Inflammation in Atherosclerosis. Circ. Res. 124 , 315–327. 10.1161/circresaha.118.313591 (2019). Laera, N. et al. Impact of Immunity on Coronary Artery Disease: An Updated Pathogenic Interplay and Potential Therapeutic Strategies. Life (Basel) . 13 10.3390/life13112128 (2023). Jaén, R. I. et al. Innate Immune Receptors, Key Actors in Cardiovascular Diseases. JACC: Basic. Translational Sci. 5 , 735–749. https://doi.org/10.1016/j.jacbts.2020.03.015 (2020). Kircheis, R. & Planz, O. The Role of Toll-like Receptors (TLRs) and Their Related Signaling Pathways in Viral Infection and Inflammation. Int. J. Mol. Sci. 24 10.3390/ijms24076701 (2023). Goulopoulou, S., McCarthy, C. G. & Webb, R. C. Toll-like Receptors in the Vascular System: Sensing the Dangers Within. Pharmacol. Rev. 68 , 142–167. 10.1124/pr.114.010090 (2016). Tanase, D. M. et al. Portrayal of NLRP3 Inflammasome in Atherosclerosis: Current Knowledge and Therapeutic Targets. Int. J. Mol. Sci. 24 10.3390/ijms24098162 (2023). Karasawa, T. & Takahashi, M. Role of NLRP3 Inflammasomes in Atherosclerosis. J. Atheroscler Thromb. 24 , 443–451. 10.5551/jat.RV17001 (2017). Bourgonje, A. R. et al. Angiotensin-converting enzyme 2 (ACE2), SARS-CoV-2 and the pathophysiology of coronavirus disease 2019 (COVID-19). J. Pathol. 251 , 228–248. 10.1002/path.5471 (2020). Liu, L. P., Zhang, X. L. & Li, J. New perspectives on angiotensin-converting enzyme 2 and its related diseases. World J. Diabetes . 12 , 839–854. 10.4239/wjd.v12.i6.839 (2021). Cao, Q. et al. Impact of Cardiovascular Diseases on COVID-19: A Systematic Review. Med. Sci. Monit. 27 , e930032. 10.12659/msm.930032 (2021). Olejarz, W., Łacheta, D. & Kubiak-Tomaszewska, G. Matrix Metalloproteinases as Biomarkers of Atherosclerotic Plaque Instability. Int. J. Mol. Sci. 21 10.3390/ijms21113946 (2020). Liu, P., Sun, M. & Sader, S. Matrix metalloproteinases in cardiovascular disease. Can J Cardiol 22 Suppl B, 25b-30b, doi: (2006). 10.1016/s0828-282x(06)70983-7 Porritt, R. A. & Crother, T. R. Chlamydia pneumoniae Infection and Inflammatory Diseases. Immunopathol. Dis. Th. 7 , 237–254. 10.1615/ForumImmunDisTher.2017020161 (2016). Muhlestein, J. B. et al. Infection With Chlamydia pneumoniae Accelerates the Development of Atherosclerosis and Treatment With Azithromycin Prevents It in a Rabbit Model. Circulation . 97 , 633–636. 10.1161/01.CIR.97.7.633 (1998). Llor, C. & Bjerrum, L. Antimicrobial resistance: risk associated with antibiotic overuse and initiatives to reduce the problem. Ther. Adv. Drug Saf. 5 , 229–241. 10.1177/2042098614554919 (2014). Muteeb, G., Rehman, M. T., Shahwan, M. & Aatif, M. Origin of Antibiotics and Antibiotic Resistance, and Their Impacts on Drug Development: A Narrative Review. Pharmaceuticals (Basel) . 16. 10.3390/ph16111615 (2023). Ahmed, S. K. et al. Antimicrobial resistance: Impacts, challenges, and future prospects. J. Med. Surg. Public. Health . 2 , 100081. https://doi.org/10.1016/j.glmedi.2024.100081 (2024). Heianza, Y. et al. Duration and life-stage of antibiotic use and risk of cardiovascular events in women. Eur. Heart J. 40 , 3838–3845. 10.1093/eurheartj/ehz231 (2019). Luong, H. X., Thanh, T. T. & Tran, T. H. Antimicrobial peptides - Advances in development of therapeutic applications. Life Sci. 260 , 118407. 10.1016/j.lfs.2020.118407 (2020). Xuan, J. et al. Antimicrobial peptides for combating drug-resistant bacterial infections. Drug Resist. Updates . 68 , 100954. https://doi.org/10.1016/j.drup.2023.100954 (2023). Benfield, A. H. & Henriques, S. T. Mode-of-Action of Antimicrobial Peptides: Membrane Disruption vs. Intracellular Mechanisms. Front. Med. Technol. 2 , 610997. 10.3389/fmedt.2020.610997 (2020). Gong, H. et al. How do antimicrobial peptides disrupt the lipopolysaccharide membrane leaflet of Gram-negative bacteria? J. Colloid Interface Sci. 637 , 182–192. https://doi.org/10.1016/j.jcis.2023.01.051 (2023). Zhang, Q. Y. et al. Antimicrobial peptides: mechanism of action, activity and clinical potential. Military Med. Res. 8 10.1186/s40779-021-00343-2 (2021). Dermawan, D., Sumirtanurdin, R. & Dewantisari, D. Simulasi dinamika molekular reseptor estrogen alfa dengan andrografolid sebagai anti kanker payudara. Indones J. Pharm. Sci. Technol. 6 , 65–76 (2019). Nnyigide, O. S., Lee, S. G. & Hyun, K. Silico Characterization of the Binding Modes of Surfactants with Bovine Serum Albumin. Sci. Rep. 9 , 10643. 10.1038/s41598-019-47135-2 (2019). Zasloff, M. Antimicrobial peptides of multicellular organisms. Nature . 415 , 389–395. 10.1038/415389a (2002). Ganz, T. Defensins: antimicrobial peptides of innate immunity. Nat. Rev. Immunol. 3 , 710–720. 10.1038/nri1180 (2003). Festa, M. et al. Cardiovascular Active Peptides of Marine Origin with ACE Inhibitory Activities: Potential Role as Anti-Hypertensive Drugs and in Prevention of SARS-CoV-2 Infection. Int. J. Mol. Sci. 21 10.3390/ijms21218364 (2020). Lee, R. T. Matrix metalloproteinase inhibition and the prevention of heart failure. Trends Cardiovasc. Med. 11 , 202–205. 10.1016/s1050-1738(01)00113-x (2001). Ndinguri, M. W., Bhowmick, M., Tokmina-Roszyk, D., Robichaud, T. K. & Fields, G. B. Peptide-based selective inhibitors of matrix metalloproteinase-mediated activities. Molecules . 17 , 14230–14248. 10.3390/molecules171214230 (2012). Al Musaimi, O., Lombardi, L., Williams, D. R. & Albericio, F. Strategies for Improving Peptide Stability and Delivery. Pharmaceuticals (Basel) . 15 10.3390/ph15101283 (2022). Hossain, M. S. et al. Therapeutic Potential of Antiviral Peptides against the NS2B/NS3 Protease of Zika Virus. ACS Omega . 8 , 35207–35218. 10.1021/acsomega.3c04903 (2023). Dermawan, D., Bahtiar, R. & Sofian, F. F. Implementation of Green Supply Chain Management (GSCM) in the pharmaceutical industry in Indonesia: feasibility analysis and case studies. J. Ilm Farm. 15 , 23–29 (2019). Kastritis, P. L. & Bonvin, A. M. On the binding affinity of macromolecular interactions: daring to ask why proteins interact. J. R Soc. Interface . 10 , 20120835. 10.1098/rsif.2012.0835 (2013). Lin, D. et al. Improved functionality and safety of peptides by the formation of peptide-polyphenol complexes. Trends Food Sci. Technol. 141 , 104193. https://doi.org/10.1016/j.tifs.2023.104193 (2023). Robles-Loaiza, A. A. et al. Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity. Pharmaceuticals (Basel) . 15 10.3390/ph15030323 (2022). Amorim, A. M. B. et al. Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction. Chem. Res. Toxicol. 37 , 827–849. 10.1021/acs.chemrestox.3c00352 (2024). Aoki, W. & Ueda, M. Characterization of Antimicrobial Peptides toward the Development of Novel Antibiotics. Pharmaceuticals (Basel) . 6 , 1055–1081. 10.3390/ph6081055 (2013). Schmidtchen, A., Pasupuleti, M. & Malmsten, M. Effect of hydrophobic modifications in antimicrobial peptides. Adv. Colloid Interface Sci. 205 10.1016/j.cis.2013.06.009 (2013). Zhu, X. et al. Characterization of antimicrobial activity and mechanisms of low amphipathic peptides with different α-helical propensity. Acta Biomater. 18 , 155–167. https://doi.org/10.1016/j.actbio.2015.02.023 (2015). Zhang, Q. Y. et al. Antimicrobial peptides: mechanism of action, activity and clinical potential. Mil Med. Res. 8 , 48. 10.1186/s40779-021-00343-2 (2021). Rotem, S., Radzishevsky, I. & Mor, A. Physicochemical properties that enhance discriminative antibacterial activity of short dermaseptin derivatives. Antimicrob. Agents Chemother. 50 , 2666–2672. 10.1128/aac.00030-06 (2006). Zainal Baharin, N. H. et al. The characteristics and roles of antimicrobial peptides as potential treatment for antibiotic-resistant pathogens: a review. PeerJ . 9 , e12193. 10.7717/peerj.12193 (2021). Amatuni, A., Shuster, A., Abegg, D., Adibekian, A. & Renata, H. Comprehensive Structure-Activity Relationship Studies of Cepafungin Enabled by Biocatalytic C-H Oxidations. ACS Cent. Sci. 9 , 239–251. 10.1021/acscentsci.2c01219 (2023). Guha, R. On Exploring Structure–Activity Relationships. Methods in molecular biology . (Clifton N J) . 993 , 81–94. 10.1007/978-1-62703-342-8_6 (2013). Fan, Z. et al. Sustained Release of a Peptide-Based Matrix Metalloproteinase-2 Inhibitor to Attenuate Adverse Cardiac Remodeling and Improve Cardiac Function Following Myocardial Infarction. Biomacromolecules . 18 , 2820–2829. 10.1021/acs.biomac.7b00760 (2017). Evans, B. J., King, A. T., Katsifis, A., Matesic, L. & Jamie, J. F. Methods to Enhance the Metabolic Stability of Peptide-Based PET Radiopharmaceuticals. Molecules . 25 10.3390/molecules25102314 (2020). Böttger, R., Hoffmann, R. & Knappe, D. Differential stability of therapeutic peptides with different proteolytic cleavage sites in blood, plasma and serum. PLoS One . 12 , e0178943. 10.1371/journal.pone.0178943 (2017). Ryan, J. T., Ross, R. P., Bolton, D., Fitzgerald, G. F. & Stanton, C. Bioactive peptides from muscle sources: meat and fish. Nutrients . 3 , 765–791. 10.3390/nu3090765 (2011). Musliha, A., Dermawan, D., Rahayu, P. & Tjandrawinata, R. R. Unraveling modulation effects on albumin synthesis and inflammation by Striatin, a bioactive protein fraction isolated from Channa striata: In silico proteomics and in vitro approaches. Heliyon 10 , 10.1016/j.heliyon.2024.e38386 (2024). Han, Y., Zhang, M., Lai, R. & Zhang, Z. Chemical modifications to increase the therapeutic potential of antimicrobial peptides. Peptides . 146 , 170666. https://doi.org/10.1016/j.peptides.2021.170666 (2021). Ding, Y. et al. Impact of non-proteinogenic amino acids in the discovery and development of peptide therapeutics. Amino Acids . 52 , 1207–1226. 10.1007/s00726-020-02890-9 (2020). Cavallaro, P. A. et al. Peptides Targeting HER2-Positive Breast Cancer Cells and Applications in Tumor Imaging and Delivery of Chemotherapeutics. Nanomaterials (Basel) . 13 10.3390/nano13172476 (2023). Ahn, W. Y. & Busemeyer, J. R. Challenges and promises for translating computational tools into clinical practice. Curr. Opin. Behav. Sci. 11 , 1–7. 10.1016/j.cobeha.2016.02.001 (2016). Mahadik, R., Kiptoo, P., Tolbert, T. & Siahaan, T. J. Immune Modulation by Antigenic Peptides and Antigenic Peptide Conjugates for Treatment of Multiple Sclerosis. Med. Res. Arch. 10 10.18103/mra.v10i5.2804 (2022). Jawa, V. et al. T-Cell Dependent Immunogenicity of Protein Therapeutics Pre-clinical Assessment and Mitigation-Updated Consensus and Review 2020. Front. Immunol. 11 , 1301. 10.3389/fimmu.2020.01301 (2020). Rossino, G. et al. Peptides as Therapeutic Agents: Challenges and Opportunities in the Green Transition Era. Molecules . 28 10.3390/molecules28207165 (2023). Wang, L. et al. Therapeutic peptides: current applications and future directions. Signal. Transduct. Target. Therapy . 7 , 48. 10.1038/s41392-022-00904-4 (2022). Guex, N. & Peitsch, M. C. SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis . 18 , 2714–2723. 10.1002/elps.1150181505 (1997). Tian, W., Chen, C., Lei, X., Zhao, J. & Liang, J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res. 46 , 363–367. 10.1093/nar/gky473 (2018). Lan, J. et al. Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature . 581 , 215–220. 10.1038/s41586-020-2180-5 (2020). Thompson, D., Pepys, M. B. & Wood, S. P. The physiological structure of human C-reactive protein and its complex with phosphocholine. Structure . 7 , 169–177. 10.1016/S0969-2126(99)80023-9 (1999). Rowsell, S. et al. Crystal Structure of Human MMP9 in Complex with a Reverse Hydroxamate Inhibitor. J. Mol. Biol. 319 , 173–181. https://doi.org/10.1016/S0022-2836(02)00262-0 (2002). Sharif, H. et al. Structural mechanism for NEK7-licensed activation of NLRP3 inflammasome. Nature . 570 , 338–343. 10.1038/s41586-019-1295-z (2019). Park, B. S. et al. The structural basis of lipopolysaccharide recognition by the TLR4–MD-2 complex. Nature . 458 , 1191–1195. 10.1038/nature07830 (2009). Wang, G., Li, Y. & Li, X. Correlation of Three-dimensional Structures with the Antibacterial Activity of a Group of Peptides Designed Based on a Nontoxic Bacterial Membrane Anchor *. J. Biol. Chem. 280 , 5803–5811. 10.1074/jbc.M410116200 (2005). et al. The Structure of Human β-Defensin-2 Shows Evidence of Higher Order Oligomerization*. Journal of Biological Chemistry 275, 32911–32918, doi:10.1074/jbc.M006098200 (2000). Zangger, K., Gößler, R., Khatai, L., Lohner, K. & Jilek, A. Structures of the glycine-rich diastereomeric peptides bombinin H2 and H4. Toxicon 52, 246–254, doi: (2008). https://doi.org/10.1016/j.toxicon.2008.05.011 Wang, G. Structures of Human Host Defense Cathelicidin LL-37 and Its Smallest Antimicrobial Peptide KR-12 in Lipid Micelles *. J. Biol. Chem. 283 , 32637–32643. 10.1074/jbc.M805533200 (2008). Oh, D. et al. NMR structural characterization of cecropin A(1–8) - magainin 2(1–12) and cecropin A (1–8) - melittin (1–12) hybrid peptides. J. Pept. Res. 53 , 578–589. 10.1034/j.1399-3011.1999.00067.x (1999). de Viana, T. et al. Release of immunomodulatory peptides at bacterial membrane interfaces as a novel strategy to fight microorganisms. J. Biol. Chem. 299 10.1016/j.jbc.2023.103056 (2023). Nguyen, V. S., Tan, K. W., Ramesh, K., Chew, F. T. & Mok, Y. K. Structural basis for the bacterial membrane insertion of dermcidin peptide, DCD-1L. Sci. Rep. 7 , 13923. 10.1038/s41598-017-13600-z (2017). Loffredo, M. R. et al. Membrane perturbing activities and structural properties of the frog-skin derived peptide Esculentin-1a(1–21)NH2 and its Diastereomer Esc(1–21)-1c: Correlation with their antipseudomonal and cytotoxic activity. Biochim. et Biophys. Acta (BBA) - Biomembr. 1859 , 2327–2339. https://doi.org/10.1016/j.bbamem.2017.09.009 (2017). Runge, S., Thøgersen, H., Madsen, K., Lau, J. & Rudolph, R. Crystal Structure of the Ligand-bound Glucagon-like Peptide-1 Receptor Extracellular Domain *. J. Biol. Chem. 283 , 11340–11347. 10.1074/jbc.M708740200 (2008). Jordan, J. B. et al. Hepcidin Revisited, Disulfide Connectivity, Dynamics, and Structure . J. Biol. Chem. 284 , 24155–24167. 10.1074/jbc.M109.017764 (2009). Mariano, G. H. et al. Characterization of novel human intragenic antimicrobial peptides, incorporation and release studies from ureasil-polyether hybrid matrix. Mater. Sci. Engineering: C . 119 , 111581. https://doi.org/10.1016/j.msec.2020.111581 (2021). Friedrich, C. L., Rozek, A., Patrzykat, A. & Hancock, R. E. W. Structure and Mechanism of Action of an Indolicidin Peptide Derivative with Improved Activity against Gram-positive Bacteria *. J. Biol. Chem. 276 , 24015–24022. 10.1074/jbc.M009691200 (2001). Hwang, P. M., Zhou, N., Shan, X., Arrowsmith, C. H. & Vogel, H. J. Three-Dimensional Solution Structure of Lactoferricin B, an Antimicrobial Peptide Derived from Bovine Lactoferrin. Biochemistry . 37 , 4288–4298. 10.1021/bi972323m (1998). Pillong, M. et al. Rational Design of Membrane-Pore-Forming Peptides. Small . 13 , 1701316. https://doi.org/10.1002/smll.201701316 (2017). Gesell, J., Zasloff, M. & Opella, S. J. Two-dimensional 1H NMR experiments show that the 23-residue magainin antibiotic peptide is an α-helix in dodecylphosphocholine micelles, sodium dodecylsulfate micelles, and trifluoroethanol/water solution. J. Biomol. NMR. 9 , 127–135. 10.1023/A:1018698002314 (1997). Terwilliger, T. C., Weissman, L. & Eisenberg, D. The structure of melittin in the form I crystals and its implication for melittin's lytic and surface activities. Biophys. J. 37 , 353–361. 10.1016/s0006-3495(82)84683-3 (1982). Mathavan, I. et al. Structural basis for hijacking siderophore receptors by antimicrobial lasso peptides. Nat. Chem. Biol. 10 , 340–342. 10.1038/nchembio.1499 (2014). Hsu, S. T. D. et al. The nisin–lipid II complex reveals a pyrophosphate cage that provides a blueprint for novel antibiotics. Nat. Struct. Mol. Biol. 11 , 963–967. 10.1038/nsmb830 (2004). Bhunia, A. et al. NMR Structure of Pardaxin, a Pore-forming Antimicrobial Peptide, in Lipopolysaccharide Micelles: MECHANISM OF OUTER MEMBRANE PERMEABILIZATION 2 . J. Biol. Chem. 285 , 3883–3895. 10.1074/jbc.M109.065672 (2010). Comert, F. et al. The host-defense peptide piscidin P1 reorganizes lipid domains in membranes and decreases activation energies in mechanosensitive ion channels. J. Biol. Chem. 294 , 18557–18570. 10.1074/jbc.RA119.010232 (2019). Amos, S. T. et al. Antimicrobial Peptide Potency is Facilitated by Greater Conformational Flexibility when Binding to Gram-negative Bacterial Inner Membranes. Sci. Rep. 6 , 37639. 10.1038/srep37639 (2016). Powers, J. P. S., Rozek, A. & Hancock, R. E. W. Structure–activity relationships for the β-hairpin cationic antimicrobial peptide polyphemusin I. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1698, 239–250, doi: (2004). https://doi.org/10.1016/j.bbapap.2003.12.009 Fahrner, R. L. et al. Solution structure of protegrin-1, a broad-spectrum antimicrobial peptide from porcine leukocytes. Chem. Biol. 3 , 543–550. 10.1016/s1074-5521(96)90145-3 (1996). Petit, V. W. et al. A hemocyanin-derived antimicrobial peptide from the penaeid shrimp adopts an alpha-helical structure that specifically permeabilizes fungal membranes. Biochim. et Biophys. Acta (BBA) - Gen. Subj. 1860 , 557–568. https://doi.org/10.1016/j.bbagen.2015.12.010 (2016). Kawulka, K. E. et al. Structure of Subtilosin A, a Cyclic Antimicrobial Peptide from Bacillus subtilis with Unusual Sulfur to α-Carbon Cross-Links: Formation and Reduction of α-Thio-α-Amino Acid Derivatives. Biochemistry . 43 , 3385–3395. 10.1021/bi0359527 (2004). Kushibiki, T. et al. Interaction between tachyplesin I, an antimicrobial peptide derived from horseshoe crab, and lipopolysaccharide. Biochim. et Biophys. Acta (BBA) - Proteins Proteom. 1844 , 527–534. https://doi.org/10.1016/j.bbapap.2013.12.017 (2014). Fujitani, N. et al. Structure of the Antimicrobial Peptide Tachystatin A *. J. Biol. Chem. 277 , 23651–23657. 10.1074/jbc.M111120200 (2002). Manzo, G. et al. Temporin L and aurein 2.5 have identical conformations but subtly distinct membrane and antibacterial activities. Sci. Rep. 9 , 10934. 10.1038/s41598-019-47327-w (2019). Mandard, N. et al. Solution structure of thanatin, a potent bactericidal and fungicidal insect peptide, determined from proton two-dimensional nuclear magnetic resonance data. Eur. J. Biochem. 256 , 404–410. https://doi.org/10.1046/j.1432-1327.1998.2560404.x (1998). Fiebig, D. et al. Destructive twisting of neutral metalloproteases: the catalysis mechanism of the Dispase autolysis-inducing protein from Streptomyces mobaraensis DSM 40487. FEBS J. 285 , 4246–4264. https://doi.org/10.1111/febs.14647 (2018). Mehdi, S. F. et al. Glucagon-like peptide-1: a multi-faceted anti-inflammatory agent. Front. Immunol. 14 , 1148209. 10.3389/fimmu.2023.1148209 (2023). Amezcua-Castillo, E. et al. C-Reactive Protein: The Quintessential Marker of Systemic Inflammation in Coronary Artery Disease-Advancing toward Precision Medicine. Biomedicines 11, doi: (2023). 10.3390/biomedicines11092444 Sortino, O. et al. The Effects of Recombinant Human Lactoferrin on Immune Activation and the Intestinal Microbiome Among Persons Living with Human Immunodeficiency Virus and Receiving Antiretroviral Therapy. J. Infect. Dis. 219 , 1963–1968. 10.1093/infdis/jiz042 (2019). Agier, J., Efenberger, M. & Brzezińska-Błaszczyk, E. Cathelicidin impact on inflammatory cells. Cent. Eur. J. Immunol. 40 , 225–235. 10.5114/ceji.2015.51359 (2015). Dominguez, C., Boelens, R. & Bonvin, A. M. J. J. HADDOCK: A Protein – Protein Docking Approach Based on Biochemical or Biophysical Information. J. Am. Chem. Soc. 125 , 1731–1737. 10.1021/ja026939x (2003). Huang, L. et al. Novel peptide inhibitors of angiotensin-converting enzyme 2. J. Biol. Chem. 278 , 15532–15540. 10.1074/jbc.M212934200 (2003). Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature . 596 , 583–589. 10.1038/s41586-021-03819-2 (2021). Vangone, A. & Bonvin, A. P. R. O. D. I. G. Y. A Contact-based Predictor of Binding Affinity in Protein-protein Complexes. BIO-PROTOCOL . 7 10.21769/BioProtoc.2124 (2017). Grassmann, G. et al. Computational Approaches to Predict Protein–Protein Interactions in Crowded Cellular Environments. Chem. Rev. 124 , 3932–3977. 10.1021/acs.chemrev.3c00550 (2024). Pronk, S. et al. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics . 29 , 845–854. 10.1093/bioinformatics/btt055 (2013). Robertson, M. J., Tirado-Rives, J. & Jorgensen, W. L. Improved Peptide and Protein Torsional Energetics with the OPLSAA Force Field. J. Chem. Theory Comput. 11 , 3499–3509. 10.1021/acs.jctc.5b00356 (2015). Alotaiq, N., Dermawan, D. & Elwali, N. E. Leveraging Therapeutic Proteins and Peptides from Lumbricus Earthworms: Targeting SOCS2 E3 Ligase for Cardiovascular Therapy through Molecular Dynamics Simulations. Int. J. Mol. Sci. 25 , 10818 (2024). Yuet, P. & Blankschtein, D. Molecular Dynamics Simulation Study of Water Surfaces: Comparison of Flexible Water Models. J. Phys. Chem. 114 , 13786–13795. 10.1021/jp1067022 (2010). Saini, R. S. et al. Dental biomaterials redefined: molecular docking and dynamics-driven dental resin composite optimization. BMC Oral Health . 24 , 557. 10.1186/s12903-024-04343-1 (2024). The PyMOL Molecular Graphics System v. 2.4. (2020). Pettersen, E. F. et al. UCSF Chimera–a visualization system for exploratory research and analysis. J. Comput. Chem. 25 , 1605–1612. 10.1002/jcc.20084 (2004). Tian, S. et al. Assessing an ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility. J. Chem. Inf. Model. 54 , 2664–2679. 10.1021/ci500414b (2014). Yuan, Z. et al. Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors. Int. J. Mol. Sci. 25 , 1–24 (2024). Rifai, E. A., Ferrario, V., Pleiss, J. & Geerke, D. P. Combined Linear Interaction Energy and Alchemical Solvation Free-Energy Approach for Protein-Binding Affinity Computation. J. Chem. Theory Comput. 16 , 1300–1310. 10.1021/acs.jctc.9b00890 (2020). Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A., Moreno, E. & gmx_MMPBSA A New Tool to Perform End-State Free Energy Calculations with GROMACS. J. Chem. Theory Comput. 17 , 6281–6291. 10.1021/acs.jctc.1c00645 (2021). Miller, B. R. 3 et al. MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. J. Chem. Theory Comput. 8 , 3314–3321. 10.1021/ct300418h (2012). Panday, S. K. & Alexov, E. Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation. ACS Omega . 7 , 11057–11067. 10.1021/acsomega.1c07037 (2022). Timmons, P. B. & Hewage, C. M. HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks. Sci. Rep. 10 , 10869. 10.1038/s41598-020-67701-3 (2020). Additional Declarations No competing interests reported. Supplementary Files SupplementaryData1MolecularDockingResults.xlsx SupplementaryData2MolecularInteractions.xlsx SupplementaryData3AntimicrobialPeptidesandReceptorsDataset.xlsx Cite Share Download PDF Status: Published Journal Publication published 14 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Dec, 2024 Reviews received at journal 24 Dec, 2024 Reviews received at journal 28 Nov, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers agreed at journal 17 Nov, 2024 Reviewers invited by journal 17 Nov, 2024 Editor assigned by journal 15 Nov, 2024 Editor invited by journal 15 Nov, 2024 Submission checks completed at journal 14 Nov, 2024 First submitted to journal 02 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5376324","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":383850841,"identity":"4e06b958-5b78-4b14-8be5-ee36cf69e8c1","order_by":0,"name":"Doni Dermawan","email":"","orcid":"","institution":"Warsaw University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Doni","middleName":"","lastName":"Dermawan","suffix":""},{"id":383850842,"identity":"d11feda2-e64a-4c7d-a315-520115020530","order_by":1,"name":"Nasser Alotaiq","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHACxgNAgoeBvbEBKpBAWM8BoCIeBp6DJGphYJCAqySgxZz98IMDH3/YyfDdfNy64WfOYQZ+9hwDxh81uLVY9qQZHJyRkMwjeTux7WbvtsMMkj1vDJh5juHWYnAgweAwTwIzjwFQyw1eoBaDGzkGzAxseLScf/4BqKWex+Dmwbabf4Fa7G+AHPYPjxagAqCWwzwGNxjbboNtkcgxYOBtw6flTcHBGWnHeSTPJLbdlt2WziNx5lnBYd4+fA5L3/jgg021Pd/x489uvt1mLcffnrzx4Y9vuLUgwAEIxYPEJlLLKBgFo2AUjAIMAADi4VsdGhSWrQAAAABJRU5ErkJggg==","orcid":"","institution":"Imam Mohammad ibn Saud Islamic University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Nasser","middleName":"","lastName":"Alotaiq","suffix":""}],"badges":[],"createdAt":"2024-11-02 04:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5376324/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5376324/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-93683-1","type":"published","date":"2025-03-14T15:57:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70371977,"identity":"07880fe4-a64c-4ee2-80dc-d922d25b7f7a","added_by":"auto","created_at":"2024-12-02 14:50:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1248955,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking simulations illustrate the optimal binding orientations for interactions between AMPs and ACE2. (a) ACE2:DX600 peptide (standard antagonist) complex. (b) ACE2:Hepcidin complex. (c) ACE2:Pleurocidin complex. (d) ACE2:Tachystatin complex. (e) ACE2:Thermolysin complex. (f) ACE2:PvHCt complex. The AMPs depicted were chosen based on their consistently high binding affinities from a broader set of 30 AMPs analyzed in this study.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5376324/v1/8d024b833b9a33de18bec3c5.png"},{"id":70373519,"identity":"31b3d8e5-a30b-41e4-b46d-17065695e650","added_by":"auto","created_at":"2024-12-02 14:58:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":466499,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking results of optimal target receptor-AMP complexes, highlighting the lowest binding energy values indicative of a superior affinity. (A) ACE2 complexes. (B) CRP complexes. (C) MMP9 complexes. (D) NLRP3 complexes. (E) TLR4 complexes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5376324/v1/f6f6495264b76c3cb12b7583.png"},{"id":70371988,"identity":"646df22d-2370-4469-9c61-50f1359dda65","added_by":"auto","created_at":"2024-12-02 14:50:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":245838,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrices illustrate the relationships between binding energy (kcal/mol) and individual energy components for each target receptor-AMP complex. These include (A) ACE2 complexes, (B) CRP complexes, (C) MMP9 complexes, (D) NLRP3 complexes, and (E) TLR4 complexes. The matrices measure the degree of association between binding energy, van der Waals energy, electrostatic energy, and desolvation energy. The correlation values range from -1 to 1, where 1 denotes a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 signifies no correlation.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5376324/v1/b0928030672968c3183957d0.png"},{"id":70371989,"identity":"98307f0d-4872-43c3-834b-fd4cc2b4b70d","added_by":"auto","created_at":"2024-12-02 14:50:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":593219,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular dynamics (MD) simulation results illustrating the Root Mean Square Fluctuation (RMSF) profiles, highlighting the flexibility of residues within different complexes: (A) ACE2 complexes, (B) CRP complexes, (C) MMP9 complexes, (D) NLRP3 complexes, and (E) TLR4 complexes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5376324/v1/b8adbbddf08ca4c0abfdce46.png"},{"id":78689055,"identity":"73b107e5-ddae-4d2b-9f5a-188bf46f495e","added_by":"auto","created_at":"2025-03-17 16:10:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6153584,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5376324/v1/ab2a4834-96aa-4595-85a5-2d8156359d85.pdf"},{"id":70371980,"identity":"822c17e1-f9dc-47d5-bb10-9c33588e109e","added_by":"auto","created_at":"2024-12-02 14:50:37","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39605,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData1MolecularDockingResults.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5376324/v1/205dea43979d6ef9991f7392.xlsx"},{"id":70373518,"identity":"dae13c89-5b3d-405e-bfed-8b931e834abe","added_by":"auto","created_at":"2024-12-02 14:58:37","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27980,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData2MolecularInteractions.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5376324/v1/6a77699a03bc344fadc71e5f.xlsx"},{"id":70371984,"identity":"585952fa-7e33-41b6-b8cc-fd595d09cb61","added_by":"auto","created_at":"2024-12-02 14:50:37","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16879,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData3AntimicrobialPeptidesandReceptorsDataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5376324/v1/9bf7e1482a0d326c1cf51bc9.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eComputational analysis of antimicrobial peptides targeting key receptors in infection-related cardiovascular diseases: Molecular docking and dynamics insights\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eInfections and cardiovascular diseases (CVDs) share a complex, bidirectional relationship. Pathogenic microorganisms can initiate or exacerbate CVDs through direct infection of cardiovascular tissues, immune system dysregulation, or the induction of chronic inflammatory states\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. For instance, respiratory pathogens such as influenza virus and bacterial pathogens like \u003cem\u003eChlamydia pneumoniae\u003c/em\u003e and \u003cem\u003eHelicobacter pylori\u003c/em\u003e have been associated with an increased risk of myocardial infarction and atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The chronic inflammation induced by these pathogens accelerates the formation of atherosclerotic plaques, a major cause of coronary artery disease. Among the most prominent mechanisms is the activation of the innate immune system via receptors such as Toll-Like Receptors (TLRs), which recognize pathogen-associated molecular patterns (PAMPs)\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. TLRs initiate signaling cascades upon activation that produce pro-inflammatory cytokines, resulting in endothelial dysfunction and plaque instability. Another key player in this inflammatory process is the NLRP3 inflammasome, which responds to infectious and non-infectious stimuli, including viral RNA, bacterial toxins, and cholesterol crystals\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Once activated, the NLRP3 inflammasome promotes the secretion of IL-1β, a potent pro-inflammatory cytokine implicated in the progression of atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Furthermore, angiotensin-converting enzyme 2 (ACE2), a receptor known for regulating blood pressure, has gained considerable attention due to its interaction with the SARS-CoV-2 virus\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The binding of the virus to ACE2 receptors impairs its physiological functions, leading to cardiovascular complications, including myocardial injury and arrhythmias\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Matrix metalloproteinases (MMPs), a family of proteolytic enzymes involved in extracellular matrix remodeling, also play a critical role in CVD progression, particularly in the degradation of the fibrous cap of atherosclerotic plaques, leading to plaque rupture and subsequent cardiovascular events\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. While MMP9 is primarily recognized as a target enzyme, it may also exhibit receptor-like characteristics through its interactions with various signaling molecules, highlighting its dual role in inflammation and cardiovascular pathology.\u003c/p\u003e \u003cp\u003eTreating infections related to cardiovascular diseases typically involves using antimicrobial agents such as antibiotics, antivirals, and antifungals, depending on the pathogen involved. For bacterial infections, antibiotics such as macrolides and β-lactams are commonly prescribed. For example, azithromycin is often used to treat \u003cem\u003eChlamydia pneumoniae\u003c/em\u003e infections associated with atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, the use of antibiotics poses several challenges, including the emergence of antibiotic resistance, which has become a significant global health threat. The overuse and misuse of antibiotics have led to the development of multidrug-resistant (MDR) strains, which are not only harder to treat but also contribute to higher mortality rates in patients with CVDs\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Moreover, the long-term use of antibiotics has been associated with adverse cardiovascular outcomes, including arrhythmias and QT interval prolongation\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. As a result, there is growing interest in exploring alternative therapeutic strategies, such as the use of antimicrobial peptides (AMPs), which have the potential to overcome the limitations of current therapies and provide more targeted interventions. One of the significant advantages of AMPs is their ability to selectively target microbial membranes, which reduces the likelihood of developing resistance compared to traditional antibiotics\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Unlike conventional antimicrobial agents that often target specific bacterial proteins or enzymes, AMPs disrupt microbial membranes by interacting with their lipid bilayers, leading to cell lysis and death\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This mode of action is less prone to resistance, as microbes would need to undergo significant changes in membrane composition to evade AMP activity\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study aimed to explore the potential of AMPs to bind with key receptors involved in infection-related CVDs, offering insights into their role in modulating receptor interactions. By employing a comprehensive \u003cem\u003ein silico\u003c/em\u003e approach, including molecular docking and molecular dynamics simulations, we assessed the interaction dynamics, stability, and binding affinities of various AMPs with critical CVD-related receptors. This study aims to provide insights into the potential role of AMPs in modulating receptor interactions, which may pave the way for the development of novel peptide-based therapies targeting infection-driven cardiovascular and inflammatory conditions.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Docking Simulations of AMPs and Receptors Implicated in Infection-related CVDs\u003c/h2\u003e \u003cp\u003eThe best binding poses of AMPs and ACE2 (as one of the target receptors) are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The HADDOCK score provided an overall measure of the docking quality by integrating both the spatial and energetic fit of the AMP to the receptor. Free binding energy, measured in kilocalories per mole, offered a quantitative assessment of the binding strength between the AMPs and the receptors\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The van der Waals and electrostatic energies were also analyzed to gain insight into the non-covalent forces driving the interactions, which are crucial for understanding how these peptides interact at the molecular level. Additionally, desolvation energy, which reflects the energy of displacing water molecules from the receptor surface upon peptide binding, was calculated to understand the binding thermodynamics\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. These results laid the groundwork for further molecular dynamics simulations to explore the stability and dynamics of the AMP-receptor complexes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the free binding energy (kcal/mol) scores for the top-performing AMPs interacting with different target proteins, focusing on Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A as consistent performers. Tachystatin demonstrated significant binding affinity across multiple targets. When docked with ACE2, Tachystatin achieved a HADDOCK score of -102.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7 a.u. and a binding energy of -10.7 kcal/mol, outperforming the standard inhibitor DX600 (-8.6 kcal/mol). The van der Waals energy was \u0026minus;\u0026thinsp;48.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 kcal/mol, and the electrostatic energy was \u0026minus;\u0026thinsp;220.9\u0026thinsp;\u0026plusmn;\u0026thinsp;32.4 kcal/mol, reflecting strong interaction forces, while the RMSD value of 1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0 \u0026Aring; indicates a relatively stable conformation. Tachystatin similarly showed strong binding with MMP9, achieving a HADDOCK score of -136.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4 a.u. and a binding energy of -12.2 kcal/mol. These results highlight Tachystatin\u0026rsquo;s consistent performance across different proteins. This interaction was considered strong based on comparative analysis with the standard inhibitor results, which provided a benchmark for evaluating the binding affinity and interaction strength of the AMPs.\u003c/p\u003e \u003cp\u003eThermolysin was also identified as a highly effective AMP, particularly with ACE2 and NLRP3. In the ACE2 complex, Thermolysin achieved a HADDOCK score of -91.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 a.u. and a binding energy of -10.7 kcal/mol, comparable to Tachystatin. Its van der Waals energy of -49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 kcal/mol and electrostatic energy of -131.8\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5 kcal/mol indicate a well-balanced interaction. Thermolysin\u0026rsquo;s performance with MMP9 also stood out, yielding a binding energy of -10.6 kcal/mol with a HADDOCK score of -114.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 a.u., and it demonstrated a stable RMSD of 1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 \u0026Aring;. Pleurocidin, another top-performing peptide, demonstrated robust binding across multiple proteins. In complex with ACE2, Pleurocidin achieved a HADDOCK score of -104.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 a.u. and a binding energy of -11.2 kcal/mol, with a van der Waals energy of -46.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 kcal/mol and an electrostatic energy of -220.6\u0026thinsp;\u0026plusmn;\u0026thinsp;23.5 kcal/mol, indicating strong non-covalent interactions. Similarly, Pleurocidin exhibited a strong interaction with MMP9, with a HADDOCK score of -143.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 a.u. and a binding energy of -9.7 kcal/mol. The stability of these interactions was underscored by an RMSD of 1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026Aring;. Subtilisin A demonstrated strong binding interactions, particularly with CRP and NLRP3. In the CRP complex, Subtilisin A achieved a HADDOCK score of -128.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 a.u. and a binding energy of -12.0 kcal/mol, significantly surpassing the binding energy of DX600 (-9.7 kcal/mol). The electrostatic energy was \u0026minus;\u0026thinsp;306.9\u0026thinsp;\u0026plusmn;\u0026thinsp;57.8 kcal/mol, indicating powerful electrostatic interactions, while the RMSD value of 0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 \u0026Aring; reflects a stable interaction. In the NLRP3 complex, Subtilisin A showed similar effectiveness, with a HADDOCK score of -138.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8 a.u. and a binding energy of -12.1 kcal/mol, demonstrating its strong and consistent performance. The results, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, demonstrate the strong binding affinity and stability of the top-performing AMPs\u0026mdash;particularly Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A\u0026mdash;toward key protein targets implicated in infection pathways related to CVDs. These AMPs exhibit the potential to inhibit receptor-mediated adhesion and signaling processes, which play a critical role in infection onset and progression. Their stable interactions with these receptors suggest promising therapeutic applications in preventing CVD-associated infections. Detailed molecular docking results are available in Supplementary Data 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular docking results of top 5 performing target protein-AMP complexes compared to the standard inhibitor (DX600 peptide).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHADDOCK score (a.u.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinding energy (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVan der Waals energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElectrostatic energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesolvation energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eACE2 Complexes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-76.8 +/- 0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-38.7 +/- 2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-79.0 +/- 28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-26.1 +/- 4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.4 +/- 0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Hepcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-96.3 +/- 7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-44.7 +/- 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-250.1 +/- 24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.1 +/- 4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5 +/- 0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-104.8 +/- 1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-46.6 +/- 6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-220.6 +/- 23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-18.0 +/- 2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-102.0 +/- 3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-48.4 +/- 4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-220.9 +/- 32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-20.0 +/- 4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5 +/- 0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-91.4 +/- 2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-49.2 +/- 2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-131.8 +/- 20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-19.0 +/- 1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7 +/- 0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:PvHCt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-94.0 +/- 2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-44.8 +/- 2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-117.5 +/- 15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-28.6 +/- 1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6 +/- 0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-90.7 +/- 8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-40.8 +/- 4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-171.9 +/- 13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-15.7 +/- 2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8 +/- 0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Nisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-79.3 +/- 4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-43.0 +/- 4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-198.5 +/- 14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1 +/- 1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.6 +/- 0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-128.1 +/- 8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-46.5 +/- 6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-306.9 +/- 57.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.6 +/- 7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6 +/- 0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Protegrin-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-92.7 +/- 4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-37.5 +/- 3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-330.3 +/- 21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.7 +/- 2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.3 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Pardaxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-102.4 +/- 2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-50.1 +/- 0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-202.1 +/- 37.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-16.5 +/- 5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7 +/- 0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Magainin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-108.2 +/- 5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-25.2 +/- 4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-352.4 +/- 45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-14.6 +/- 1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 +/- 0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMP9 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-98.3 +/- 2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-55.5 +/- 3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-205.3 +/- 34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.0 +/- 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.4 +/- 0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-136.4 +/- 3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-85.2 +/- 7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-235.8 +/- 14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-28.6 +/- 3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6 +/- 0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-114.4 +/- 3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-70.3 +/- 6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-173.2 +/- 46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-27.6 +/- 3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2 +/- 0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Beta-defensin 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-93.6 +/- 4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-55.2 +/- 3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-285.5 +/- 37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.9 +/- 1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5 +/- 0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Exendin-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-48.2 +/- 3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-47.3 +/- 5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-55.4 +/- 20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-14.5 +/- 1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9 +/- 0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-143.7 +/- 4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-62.7 +/- 6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-406.5 +/- 40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-27.8 +/- 2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLRP3 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-87.9 +/- 2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-50.1 +/- 6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-237.3 +/- 44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.9 +/- 4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9 +/- 0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-138.7 +/- 2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-75.8 +/- 4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-271.9 +/- 59.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-31.2 +/- 6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7 +/- 0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Dermcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-98.6 +/- 14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-45.9 +/- 4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-374.3 +/- 87.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.9 +/- 4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 +/- 0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-100.1 +/- 8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-50.4 +/- 9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-281.3 +/- 36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2 +/- 5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.4 +/- 0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-82.3 +/- 4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-51.1 +/- 4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-193.6 +/- 19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3 +/- 2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2 +/- 0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-110.5 +/- 9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-63.0 +/- 6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-253.8 +/- 21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-15.0 +/- 6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1 +/- 0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTLR4 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-78.4 +/- 4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-41.0 +/- 7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-111.4 +/- 34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.9 +/- 5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.3 +/- 0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-125.7 +/- 2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-71.8 +/- 3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-172.7 +/- 15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-23.1 +/- 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4 +/- 0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Dermcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-91.1 +/- 8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-57.5 +/- 3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-137.5 +/- 37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.2 +/- 1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 +/- 0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-108.7 +/- 3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-59.9 +/- 2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-70.5 +/- 21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-40.4 +/- 3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.3 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Nisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-94.6 +/- 6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-55.8 +/- 5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-153.0 +/- 28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-13.6 +/- 3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.3 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Chim2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-93.3 +/- 4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-47.7 +/- 5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-119.0 +/- 23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-27.3 +/- 5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4 +/- 0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe correlation matrix depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a detailed analysis of the interplay between different energy components\u0026mdash;van der Waals energy, electrostatic energy, and desolvation energy\u0026mdash;and their contributions to the binding energy of AMP-receptor complexes, specifically in receptors associated with infection-related CVDs. This matrix is essential for understanding the nuances of molecular interactions that govern the stability and affinity of AMP binding to these receptors. The correlation coefficients, ranging from \u0026minus;\u0026thinsp;1 to 1, indicate the strength and direction of the relationships between binding energy and individual energy components. Positive values suggest a direct relationship, while negative values indicate an inverse relationship, offering valuable insights into the binding mechanisms of these AMP-receptor complexes. In the ACE2 complexes, a moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.68) between binding energy and van der Waals energy suggests that van der Waals interactions significantly stabilize these complexes. This implies that the physical interactions between the AMP and the ACE2 receptor are primarily driven by non-covalent van der Waals forces, contributing to a strong binding affinity. In contrast, electrostatic energy (r\u0026thinsp;=\u0026thinsp;0.12) and desolvation energy (r\u0026thinsp;=\u0026thinsp;0.31) show much weaker correlations, indicating that these energy components have a minimal impact on the overall binding energy in ACE2 complexes. The dominance of van der Waals interactions in these complexes suggests that designing AMP-based therapies targeting ACE2 receptors for preventing infection-related CVDs should prioritize optimizing hydrophobic and steric interactions to enhance binding stability.\u003c/p\u003e \u003cp\u003eFor the C-reactive protein (CRP) complexes, van der Waals energy (r\u0026thinsp;=\u0026thinsp;0.61) also shows a significant positive correlation, reinforcing the importance of these interactions in maintaining strong AMP-receptor binding. However, electrostatic energy exhibits a negative correlation (r = -0.27), suggesting that unfavorable electrostatic interactions may slightly weaken the binding affinity. The relatively modest positive correlation for desolvation energy (r\u0026thinsp;=\u0026thinsp;0.29) indicates that solvation effects do not play a significant role in these complexes. The results imply that, while van der Waals forces are crucial, electrostatic repulsion may limit the binding efficiency of AMPs to CRP receptors. In the case of MMP9 receptor complexes, van der Waals energy (r\u0026thinsp;=\u0026thinsp;0.39) shows a weaker correlation with binding energy than the other receptors, suggesting a reduced contribution of hydrophobic interactions to the binding affinity. Both electrostatic energy (r = -0.13) and desolvation energy (r\u0026thinsp;=\u0026thinsp;0.019) display near-zero correlations, indicating that these forces have a negligible impact on binding stability. This suggests that, for MMP9 complexes, neither van der Waals nor electrostatic interactions are particularly dominant. This may point to other factors, such as peptide conformation or flexibility, playing a more prominent role in binding affinity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNLRP3 complexes present a more balanced interaction profile, with van der Waals energy (r\u0026thinsp;=\u0026thinsp;0.44) and electrostatic energy (r\u0026thinsp;=\u0026thinsp;0.32) showing moderate positive correlations. In contrast, desolvation energy (r = -0.39) exhibits a strong negative correlation. This indicates that while van der Waals and electrostatic interactions contribute to binding stability, desolvation effects may destabilize these complexes. The negative impact of desolvation energy could arise from the displacement of water molecules around the receptor site, destabilizing the AMP-receptor complex. Optimizing AMPs for NLRP3 could involve minimizing the unfavorable desolvation contributions while enhancing van der Waals and electrostatic interactions. Lastly, the TLR4 complexes reveal a strong positive correlation between binding energy and van der Waals energy (r\u0026thinsp;=\u0026thinsp;0.61), similar to the ACE2 and CRP complexes. This suggests that van der Waals forces are again crucial in stabilizing the AMP-TLR4 complexes. However, electrostatic energy (r = -0.032) shows a near-zero correlation, indicating minimal electrostatic contributions to the overall binding energy. The desolvation energy (r\u0026thinsp;=\u0026thinsp;0.15) exhibits a weak positive correlation, suggesting that solvation effects play a relatively minor role in these complexes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIntermolecular contacts and non-interacting surface areas for receptors associated with infection-related CVD complexes with standard inhibitor and antimicrobial peptides. This table highlights the specific interactions and spatial characteristics between the receptors and both the standard inhibitor (DX600 peptide) and the selected antimicrobial peptides (AMPs), aiding in the evaluation of their binding efficacy and potential therapeutic applications.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICs charged-charged\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICs charged-polar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICs charged-apolar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eICs polar-polar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICs polar-apolar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eICs apolar-apolar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNIS charged\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNIS apolar\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eACE2 Complexes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Hepcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:PvHCt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Nisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Protegrin-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Pardaxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Magainin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMP9 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Beta-defensin 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Exendin-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLRP3 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Dermcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTLR4 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Dermcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Nisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Chim2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u0026bull; ICs: Number of intermolecular contacts\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u0026bull; NIS: Non-interacting surface\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe intermolecular contact (IC) and non-interacting surface (NIS) data in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a detailed assessment of the molecular interactions between receptors associated with infection-related CVDs and various AMPs, compared to the standard inhibitor DX600. This analysis highlights the unique interaction profiles of several top-performing AMPs, including Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A, across different receptors such as ACE2, CRP, MMP9, NLRP3, and TLR4. These AMPs exhibit consistent binding performance, demonstrated through favorable intermolecular contacts across various energy categories and receptor sites, positioning them as potential inhibitors for preventing infection-related CVDs. For the ACE2 receptor, Pleurocidin, Tachystatin, and Thermolysin demonstrate higher charged-apolar and polar-apolar interactions than standard inhibitor DX600. Pleurocidin, with 23 charged-apolar and 11 polar-apolar contacts, indicates strong hydrophobic and polar interactions, suggesting a robust binding stability with ACE2. Tachystatin also presents significant charged-polar (9) and polar-apolar (12) interactions, enhancing its ability to form stable complexes. Thermolysin, with the highest charged-apolar interactions (29), further highlights its potential to form energetically favorable contacts with the receptor. These enhanced interactions, alongside relatively stable NIS values for all three AMPs, suggest that Pleurocidin, Tachystatin, and Thermolysin are strong candidates for inhibiting ACE2-mediated infection pathways in CVDs. Subtilisin A and Pleurocidin stand out in CRP receptor complexes with substantial intermolecular contacts. Subtilisin A exhibits 29 charged-apolar, 23 polar-apolar, and 14 apolar-apolar interactions, outperforming the standard DX600 in every category. This strong interaction profile and a high NIS apolar value (43.98) suggest that Subtilisin A can efficiently block CRP's role in infection processes linked to CVDs. Pleurocidin also shows considerable interaction strengths, with 24 charged-apolar and 18 polar-apolar contacts, making it a competitive AMP in the CRP complex. Both peptides demonstrate high binding affinity, which could disrupt CRP's function in inflammatory responses associated with CVDs.\u003c/p\u003e \u003cp\u003eFor the MMP9 receptor, Thermolysin and Pleurocidin again show superior performance. Thermolysin, with 29 charged-apolar and 11 polar-apolar interactions, demonstrates a clear advantage in forming hydrophobic interactions crucial for MMP9 inhibition. Pleurocidin, with 27 charged-apolar interactions, also shows strong binding potential, supported by this complex's highest apolar-apolar contact count (34). These AMPs outperform the standard inhibitor DX600, which has only 26 charged-apolar contacts, suggesting that Thermolysin and Pleurocidin can better interfere with MMP9's role in infection-related tissue damage in CVDs. In the NLRP3 receptor, Tachystatin, Thermolysin, and Pleurocidin again exhibit consistently strong binding characteristics. Tachystatin displays many polar-polar (5) and apolar-apolar (11) contacts, reinforcing its stability within the NLRP3 complex. Thermolysin, with 31 charged-apolar and 8 polar-apolar interactions, showcases its significant hydrophobic interaction profile, while Pleurocidin leads with the highest charged-apolar contact count (31), emphasizing its binding efficiency. These interaction profiles indicate that these AMPs can effectively inhibit NLRP3, potentially reducing its involvement in inflammatory responses during infection-related CVDs. Finally, in TLR4 receptor complexes, Tachystatin, Subtilisin A, and Pleurocidin demonstrate strong intermolecular contacts. Tachystatin, with 24 charged-apolar and 26 polar-apolar contacts, highlights its capacity to engage with both charged and polar regions of the receptor. With 28 apolar-apolar interactions, Subtilisin A presents a solid hydrophobic binding potential. In contrast, Pleurocidin\u0026rsquo;s interaction profile, including 20 charged-apolar and 25 polar-apolar contacts, shows its versatility in forming intermolecular bonds. These interactions and comparable NIS values suggest that these AMPs can effectively inhibit TLR4-mediated infection pathways, often linked to inflammation and cardiovascular complications. Detailed molecular interaction results are available in Supplementary Data 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed examination of hydrogen bond interactions between receptors associated with infection-related CVD complexes with standard inhibitor and antimicrobial peptides.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidue (Receptor)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein Atom\u003c/p\u003e \u003cp\u003e(Receptor)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResidue\u003c/p\u003e \u003cp\u003e(Interacting Peptide)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProtein Atom\u003c/p\u003e \u003cp\u003e(Interacting Peptide)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInteraction Distance (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eACE2:Hepcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSer19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLys24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLys24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsp30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsp30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsp38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLys18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCRP:Nisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsn61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCys19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLys22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAsn20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGln150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGly18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGln150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCys19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003eMMP9:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeu6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyr179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePro180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThr20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsp182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGly183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsp185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeu188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTyr38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGln199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGln199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyr393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThr37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHis411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHis411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAsn10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSer412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSer412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eNLRP3:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGln147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLys2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAla5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCys7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLys164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrp34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCys13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArg452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlu23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArg452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlu23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArg502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eTLR4:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsn383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsn383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArg30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSer386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTyr44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLys435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCys23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLys435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCys24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLys435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeu27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHis458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVal12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArg460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGly17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a detailed examination of hydrogen bond interactions between infection-related CVD receptor complexes and AMPs compared with the standard inhibitor. For the ACE2 complex, strong interactions were observed, with the shortest hydrogen bond distance being 2.58 \u0026Aring; between Glu23 of ACE2 and Lys24 of Hepcidin. In the CRP complex, significant interactions include the bond between Glu147 of CRP and Lys22 of Nisin at a distance of 2.66 \u0026Aring;. MMP9 shows multiple hydrogen bonds, with a prominent bond between Glu111 of MMP9 and Leu6 of Tachystatin at 2.66 \u0026Aring;, indicating stable interaction. Similarly, the NLRP3 A complex reveals strong binding with the shortest bond between Arg452 of NLRP3 and Glu23 of Subtilisin A, both at 2.61 \u0026Aring;. Finally, TLR4 interactions show consistent hydrogen bonding, particularly between His458 of TLR4 and Val12 of Tachystatin at 2.69 \u0026Aring;, contributing to the peptide\u0026rsquo;s binding efficacy. These hydrogen bonds indicate that the antimicrobial peptides, particularly Tachystatin and Subtilisin A, form strong and stable interactions with their respective receptors, comparable to or exceeding the standard inhibitor.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular Dynamics (MD) Simulations\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e overviews the time-averaged structural properties obtained from molecular dynamics (MD) simulations of target receptor-AMP complexes. The data reveal that Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A exhibit significant structural stability and binding characteristics compared to the standard inhibitor, DX600 peptide, across various receptors associated with infection-related CVDs. For ACE2 complexes, Tachystatin shows a higher average RMSD (3.327 \u0026Aring;) and average RMSF (0.747 \u0026Aring;) compared to the standard inhibitor DX600 peptide (RMSD: 3.559 \u0026Aring;, RMSF: 0.792 \u0026Aring;), indicating a slight increase in conformational fluctuations and structural deviation. However, Tachystatin has the highest average number of hydrogen bonds (53) and the most favorable potential energy (-660,317.634 kcal/mol), suggesting that it forms more stable and energetically favorable interactions with ACE2 than DX600 peptide.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTime-averaged structural properties obtained from the MD simulations of target receptor-AMP complexes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage RMSD (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage RMSF (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage RoG (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of Hydrogen Bonds Between the Two Proteins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePotential Energy (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eACE2 Complexes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2 (apo-protein)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-440,543.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-583,916.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Hepcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-477,259.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-466,473.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-660,317.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-572,443.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:PvHCt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-603,748.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (apo-protein)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-136,101.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-184,719.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Nisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-288,567.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-166,979.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Protegrin-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-179,352.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Pardaxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-143,351.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Magainin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-145,486.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMP9 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9 (apo-protein)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-111,366.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-164,237.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-195,363.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-131,816.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Beta-defensin 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-163,272.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Exendin-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-191,827.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-119,726.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLRP3 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3 (apo-protein)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-900,750.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1,127,743.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1,070,921.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Dermcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1,370,475.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1,467,888.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1,239,438.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-977,382.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTLR4 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4 (apo-protein)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-718,765.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-755,432.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-780,309.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Dermcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-708,531.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-711,005.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Nisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-712,561.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Chim2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-743,878.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn CRP complexes, Subtilisin A and Pleurocidin display better structural stability with average RMSD values of 1.977 \u0026Aring; and 2.313 \u0026Aring;, respectively, compared to the standard inhibitor DX600 peptide (2.402 \u0026Aring;). Subtilisin A also has a comparable number of hydrogen bonds (16) and lower potential energy (-166,979.914 kcal/mol) than DX600 peptide (-184,719.286 kcal/mol), indicating efficient binding and favorable energetics. Pleurocidin exhibits a similar number of hydrogen bonds (16) and lower potential energy (-143,351.144 kcal/mol) than the DX600 peptide. For MMP9 complexes, Tachystatin and Thermolysin demonstrate lower average RMSD values (2.209 \u0026Aring; and 1.992 \u0026Aring;, respectively) compared to the standard inhibitor DX600 peptide (2.555 \u0026Aring;), indicating better structural stability. Tachystatin has a higher average number of hydrogen bonds (13) and more favorable potential energy (-195,363.451 kcal/mol) compared to DX600 peptide (-164,237.839 kcal/mol), suggesting that Tachystatin provides more stable and energetically favorable interactions with MMP9. In NLRP3 complexes, Tachystatin and Subtilisin A exhibit better structural stability with average RMSD values of 3.459 \u0026Aring; and 3.621 \u0026Aring;, respectively, compared to the standard inhibitor DX600 peptide (3.636 \u0026Aring;). Tachystatin also shows a higher number of hydrogen bonds (49) and the most favorable potential energy (-1,467,888.897 kcal/mol) among the peptides tested, indicating that it forms highly stable and energetically favorable interactions with NLRP3 compared to DX600 peptide. Finally, in TLR4 complexes, Tachystatin and Subtilisin A have lower average RMSD values (2.914 \u0026Aring; and 3.106 \u0026Aring;) compared to the standard inhibitor DX600 peptide (2.968 \u0026Aring;). Tachystatin also exhibits a higher number of hydrogen bonds (46) and more favorable potential energy (-780,309.925 kcal/mol) than DX600 peptide (-755,432.195 kcal/mol), suggesting that Tachystatin provides a more stable and energetically favorable binding interaction with TLR4.\u003c/p\u003e \u003cp\u003eRMSF values offer a detailed view of residue flexibility within receptors associated with infection-related CVD-AMP complexes, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results indicate that the AMPs exhibit a significant correspondence with the standard inhibitor, DX600 peptide, regarding residue flexibility, suggesting that these AMPs can disrupt receptor stability similarly to the standard inhibitor. The ACE2 complexes' RMSF patterns of Pleurocidin, Tachystatin, and Thermolysin closely resemble those of the standard inhibitor, particularly within residues Asn330 to Asp355. In CRP complexes, the RMSF values of these AMPs show a strong correlation with the standard inhibitor around residues Ala55 to Ile65 and Glu130 to Asp155. For MMP9 complexes, the RMSF profiles of Pleurocidin, Tachystatin, and Thermolysin match those of the standard inhibitor in residues Ile125 to Asp138. In the NLRP3 and TLR4 complexes, the RMSF values of the AMPs align closely with those of the standard inhibitor in crucial binding regions, including residues Ser161 to His175 and Lys375 to Asn400 for NLRP3, and Ser360 to Leu380 and Gln510 to Leu535 for TLR4. Overall, the RMSF data highlight that the AMPs can disrupt receptor stability in a manner similar to the standard inhibitor. The ability of these AMPs to induce comparable flexibility in critical binding regions underscores their potential as effective disruptors of receptor stability, akin to the DX600 peptide.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMolecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) Calculations\u003c/h3\u003e\n\u003cp\u003eThe binding affinities of selected AMPs for target receptors were assessed using MM/PBSA calculations (based on the MD simulation), with results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Among the peptides evaluated, Tachystatin, Pleurocidin, and Subtilisin A emerged as the most consistent in exhibiting favorable binding energies. For ACE2 complexes, Tachystatin stands out with an average binding energy of -61.58 kcal/mol, significantly more favorable than the standard inhibitor DX600 peptide, which has an average binding energy of -22.28 kcal/mol. Pleurocidin also shows strong binding with an average energy of -46.58 kcal/mol, while Subtilisin A\u0026rsquo;s binding affinity is slightly less favorable at -44.82 kcal/mol. These results suggest that Tachystatin and Pleurocidin exhibit superior binding capabilities compared to the standard inhibitor, with Tachystatin showing the most significant potential. In CRP complexes, Subtilisin A exhibits the most favorable binding energy with an average of -70.71 kcal/mol, followed by Protegrin-1 at -67.56 kcal/mol and Nisin at -38.73 kcal/mol. This contrasts with the standard inhibitor DX600 peptide, which has an average binding energy of -27.99 kcal/mol. The superior binding energy of Subtilisin A and Protegrin-1 in CRP complexes underscores their effectiveness compared to the standard inhibitor.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTime-averaged structural properties obtained from the MD simulations of target receptor-AMP complexes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMM/PBSA Calculation Results ΔG\u003csub\u003ebinding\u003c/sub\u003e (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAverage (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eACE2 Complexes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-22.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-22.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-22.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-22.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Hepcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-53.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-53.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-52.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-53.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-46.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-46.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-46.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-46.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-62.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-60.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-61.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-61.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-44.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-45.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-44.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-44.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2:PvHCt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-31.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-31.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-31.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-31.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-28.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-27.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-27.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-27.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Nisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-38.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-38.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-38.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-38.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-70.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-70.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-70.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-70.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Protegrin-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-67.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-67.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-67.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-67.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Pardaxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-60.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-56.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-60.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-59.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP:Magainin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-53.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-53.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-53.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-53.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMP9 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-53.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-53.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-51.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-52.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-96.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-96.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-96.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-96.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-66.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-68.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-65.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-66.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Beta-defensin 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-78.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-78.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-77.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-78.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Exendin-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-23.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-23.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-23.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-23.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-94.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-94.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-93.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-94.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLRP3 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-43.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-45.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-43.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-44.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-69.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-71.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-72.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-71.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Dermcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-61.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-62.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-62.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-62.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-69.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-69.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-69.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-69.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Thermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-28.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-28.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-28.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-28.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3:Pleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-60.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-55.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-60.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-58.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTLR4 Complexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:DX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-33.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-32.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-32.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-32.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Tachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-59.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-59.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-59.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-59.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Dermcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-45.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-46.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-44.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-45.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Subtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-43.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-42.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-43.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-43.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Nisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-56.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-57.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-56.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-56.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4:Chim2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-57.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-58.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-58.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-58.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor MMP9 complexes, Tachystatin again demonstrates the highest binding affinity with an average energy of -96.61 kcal/mol, followed closely by Pleurocidin at -94.24 kcal/mol. These values are significantly lower (more favorable) than the standard inhibitor DX600 peptide, which has an average energy of -52.61 kcal/mol. The binding energies of Tachystatin and Pleurocidin indicate their strong interaction with MMP9, surpassing that of the standard inhibitor. In NLRP3 complexes, Subtilisin A and Tachystatin exhibit comparable binding affinities with averages of -71.12 kcal/mol and \u0026minus;\u0026thinsp;69.20 kcal/mol, respectively, outperforming the standard inhibitor DX600 peptide, which has an average of -44.35 kcal/mol. This highlights the superior binding potential of Subtilisin A and Tachystatin for NLRP3. Finally, in TLR4 complexes, Tachystatin displays a favorable binding energy of -59.69 kcal/mol, more favorable than the standard inhibitor DX600 peptide, with an average energy of -32.82 kcal/mol. Subtilisin A\u0026rsquo;s average binding energy is -43.24 kcal/mol, indicating that it also binds effectively, though less so than Tachystatin.\u003c/p\u003e\n\u003ch3\u003eHaemolytic Activity Prediction of Antimicrobial Peptides (AMPs)\u003c/h3\u003e\n\u003cp\u003eThe DX600 peptide, which serves as the standard inhibitor in this study, exhibited a very low PROB score of 0.004, indicating a strong likelihood of being non-hemolytic. This suggests that DX600 may be a safer therapeutic candidate when considering the potential for hemolysis, particularly in clinical settings. In contrast, several peptides, such as Beta-defensin 2 (PROB\u0026thinsp;=\u0026thinsp;0.967), Chim2 (PROB\u0026thinsp;=\u0026thinsp;0.973), and Pardaxin (PROB\u0026thinsp;=\u0026thinsp;0.988), showed significantly higher PROB scores, indicating a substantial risk of hemolytic activity. This finding raises important considerations for their therapeutic application, as hemolytic peptides may lead to adverse effects in vivo, potentially limiting their clinical utility. Among the AMPs evaluated, Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A emerged as the most promising candidates based on their molecular docking and MD simulations, alongside their relatively low PROB scores, indicating a lower risk of hemolysis (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Tachystatin demonstrated a PROB score of 0.576, indicating a moderate hemolytic activity risk. Thermolysin exhibited a PROB score of 0.288, suggesting a lower hemolytic activity likelihood than many other peptides. Pleurocidin, with a PROB score of 0.095, displayed the lowest potential for hemolysis among the evaluated peptides. Subtilisin A, with a PROB score of 0.049, also showed a very low likelihood of hemolytic activity. Their favorable interaction profiles and stability in complexes with target receptors enhance their appeal as therapeutic candidates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHaemolytic activity prediction of selected AMPs. The table presents the antimicrobial peptides' sequences and their corresponding PROB scores, which indicate their likelihood of causing haemolysis; lower scores suggest a reduced potential for toxicity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntimicrobial Peptide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enTer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecTer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePROB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDX600 peptide (standard inhibitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDYSHCSPLRYYPWWKCTYPDPEGGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAurein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLFDIIKKIAESF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-defensin 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRVLYLLFSFLFIFLMPLPGVFGGIGDPVTCLKSGAICHPVFCPRRYKQIGTCGLPGTKCCKKP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombinin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIIGPVLGLVGSALGGLLKKIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCathelicidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCecropin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKWKLFKKIGKFLHSAKKF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChim2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKWAVKIIRKFIKGFISGGKRWKYM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDermcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSLLEKGLDGAKKAVGGLGKLGKDAVEDLESVGKGAVHDVKDVLDSVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsculentin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGIFSKLAGKKIKNLLISGLKNVGKEVGMDVVRTGIDIAGCKIKGEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExendin-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDLSKQMEEEAVRLFIEWLKNGGPSSGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHFPICIFCCGCCHRSKCGMCCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLMGLFNRIIRKVVKLFN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndolicidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eILAWKWAWWAWRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactoferrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFKCRRWQWRMKKLGAPSITCVRRAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLavracin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDPYFAGVKKLTKAILAVRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagainin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGIGKFLHSAKKFGKAFVGEIMNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelittin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMKFLVNVALVFMVVYISYIYAAPEPEPAPEPEAEADAEADPEAGIGAVLKVLTTGLPALISWIKRKRQQG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicrocin J25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGAGHVPEYFVGIGTPISFYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSTKDFNLDLVSVSKKDSGASPRITSISLCTPGCKTGALMGCNMKTATCNCSIHVSK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePardaxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFFALIPKIISSPLFKTLLSAVGSALSSSGGQE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePiscidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFIHHIFRGIVHAGRSIGRFLTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWGSFFKKAAHVGKHVGKAALTHYL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyphemusin I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRRWCFRVCYRGFCYRKCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtegrin-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRGGRLCYCRRRFCVCVGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePvHCt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFQDLPNFGHIQVKVFNHGEHIHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNKGCATCSIGAACLVDGPIPDFEIAGATGLFGLWG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTachyplesin-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKWCFRVCYRGICYRRCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMKLQNTLILIGCLFLMGAMIGDAYSRCQLQGFNCVVRSYGLPTIPCCRGLTCRSYFPGSTYGRCQRY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporin-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFVQWFSKFLGRIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThanatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSKKPVPIIYCNRRTGKCQRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGIGRDKLGKIFYRALTQYLTPTSNFSQLRAAAVQSATDLYGSTSQEVASVKQAFDAVGV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: The PROB score is a normalized sigmoid value that ranges from 0 to 1. A score of 0 indicates a high likelihood of being non-hemolytic, while a score of 1 suggests a high likelihood of being hemolytic. nTer is the N-terminal of a peptide, indicating the beginning of the peptide chain, which has a free amino group (-NH₂). cTer is the C-terminal of a peptide, indicating the end of the peptide chain, which has a free carboxyl group (-COOH).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eKey Findings from Molecular Simulations and Their Correlation with Other Research\u003c/h2\u003e \u003cp\u003eThe molecular docking and dynamics simulations conducted in this study provided significant insights into the interactions between AMPs and receptors implicated in infection-related CVDs. These findings highlight the potential of AMPs as therapeutic agents and contribute to our understanding of their binding mechanisms and stability compared to conventional inhibitors. Our simulations highlight that several AMPs\u0026mdash;Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A\u0026mdash;demonstrate significant binding affinity towards key receptors such as ACE2, MMP9, CRP, NLRP3, and TLR4. These peptides exhibited strong binding interactions, as indicated by favorable HADDOCK scores and binding energies. Notably, Tachystatin and Thermolysin showed particularly strong binding with ACE2, and Pleurocidin performed well with multiple receptors, including ACE2 and MMP9. These findings are consistent with previous studies that have demonstrated the potential of AMPs to modulate receptor activity in various disease contexts\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In relation to prior research, Tachystatin's superior binding affinity for ACE2 aligns with a finding that peptides targeting ACE2 could modulate its activity and influence cardiovascular disease outcomes \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Similarly, the strong binding of Thermolysin and Pleurocidin to MMP9 and CRP reflects their potential to target inflammation-related pathways. These corroborating studies emphasize the role of peptides in controlling matrix metalloproteinase activity and inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. MD simulations provided insights into the stability and conformational dynamics of AMP-receptor complexes. Tachystatin and Pleurocidin, in particular, displayed favorable structural stability with lower RMSD and RMSF values compared to the standard inhibitor DX600. These observations are supported by previous studies showing the importance of peptide stability in maintaining therapeutic efficacy\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The consistent hydrogen bonding patterns and favorable potential energies observed in our simulations underscore the potential of these AMPs to form stable and energetically favorable interactions with their targets. Furthermore, our results suggest that van der Waals interactions are crucial for binding stability, as corroborated by studies that emphasize the role of non-covalent interactions in peptide-receptor binding \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Our molecular simulations provide valuable insights into the binding mechanisms and stability of AMPs interacting with infection-related CVD receptors. The results validate previous research on peptide-receptor interactions and highlight the potential of these AMPs as therapeutic candidates for modulating infection-related pathways in CVDs.\u003c/p\u003e \u003cp\u003eMoreover, we conducted a toxicity assessment through haemolytic activity prediction using the HAPPENN tool, which leverages advanced neural network algorithms to predict the potential for hemolytic activity in therapeutic peptides. This analysis provided valuable insights into the safety profiles of the evaluated AMPs, which are crucial for their development as therapeutic agents. Hemolysis, the rupture of red blood cells, can lead to serious side effects and is a significant concern when considering the therapeutic application of peptides\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Our findings reveal variability in the hemolytic activity among the tested peptides, with some exhibiting a favorable safety profile while others raised concerns regarding their potential toxicity. For instance, certain standard inhibitors demonstrated low hemolytic activity. Such variations underscore the complexity of peptide interactions with host cells and highlight the importance of thorough toxicity assessments in the early stages of drug development\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This observation is consistent with existing literature, which indicates that the toxicity of AMPs can vary significantly based on their structure and composition. For example, several studies have reported that modifications in the amino acid sequence, charge distribution, and hydrophobicity of AMPs can dramatically influence their hemolytic activity and overall safety\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Research has shown that AMPs with a high proportion of cationic residues tend to have increased hemolytic effects due to their ability to disrupt membrane integrity\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Conversely, modifications to reduce the cationic charge can lead to decreased hemolytic activity without compromising antimicrobial efficacy, providing a dual advantage in therapeutic applications \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In addition, the potential for AMPs to harm host cells at elevated concentrations has been widely documented \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. This has prompted researchers to explore strategies for optimizing peptide design to enhance therapeutic efficacy while minimizing cytotoxic effects. For instance, some studies have focused on developing peptide analogues with reduced cytotoxicity through structure-activity relationship (SAR) analyses, enabling the identification of variants with improved safety profiles\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical Implications and Limitations\u003c/h3\u003e\n\u003cp\u003eExploring peptide-receptor interactions has significant clinical implications, particularly in developing targeted therapies and precision medicine. Understanding the fundamental mechanisms by which peptides bind to their receptors provides crucial insights for designing novel therapeutics that target specific biological pathways more effectively. For instance, inhibiting MMPs by peptides has been shown to play a critical role in managing cardiovascular diseases by preventing the degradation of extracellular matrix components, thereby improving vascular stability and function\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Similarly, peptides that target ACE2 could potentially modulate cardiovascular disease outcomes, offering new avenues for treatment\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. These findings underscore the importance of peptide-receptor interactions in crafting targeted therapeutic interventions. However, several limitations associated with peptide-based therapies must be addressed. One primary challenge is the stability of peptides in physiological conditions, which often impacts their efficacy. Peptides are susceptible to rapid degradation by proteolytic enzymes, reducing their therapeutic potential and necessitating the development of strategies to enhance their stability\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. For instance, many peptides can be hydrolyzed by enzymes like trypsin and chymotrypsin, which are abundant in the digestive system\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. This enzymatic action can result in the loss of the peptides' bioactive properties before they reach their intended target sites. Moreover, the rapid turnover and clearance of peptides from the systemic circulation can limit their effective concentration at the site of action, necessitating frequent dosing to achieve desired therapeutic outcomes\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Therefore, it is crucial to develop strategies to enhance peptide stability and resistance to enzymatic degradation. Such strategies may include chemical modifications, such as incorporating non-proteogenic amino acids or using cyclization to create more stable structures\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Additionally, the specificity of peptide-receptor interactions is crucial for minimizing off-target effects and maximizing therapeutic outcomes\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. While advances in computational modeling and simulations have improved our understanding of these interactions, translating these findings into clinical applications remains challenging\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Another limitation is the potential for adverse immune responses to peptide-based therapies. Peptides can sometimes trigger immune reactions, leading to reduced patient efficacy or adverse effects\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Furthermore, the cost and complexity of developing and producing peptide-based drugs can be significant, potentially limiting their accessibility and widespread use\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Therefore, while peptide-based therapeutics offer promising prospects, ongoing research and development are needed to address these limitations and optimize their clinical application.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, the molecular docking and dynamics simulations provide compelling insights into the interactions of AMPs with receptors implicated in infection-related CVDs. Our simulations indicated that Tachystatin, Thermolysin, Pleurocidin, and Subtilisin A exhibited favorable binding affinities and stability with key receptors such as ACE2, CRP, MMP9, NLRP3, and TLR4 compared to the standard inhibitor DX600. Tachystatin demonstrated consistent and strong binding with multiple receptors, including ACE2 and MMP9, with favorable HADDOCK scores and binding energies. Thermolysin and Pleurocidin also showed potential, exhibiting significant interaction profiles and stability across different receptor complexes. Subtilisin A, while showing robust performance with CRP and NLRP3, demonstrated impressive binding characteristics with TLR4. The detailed analysis of energy components, intermolecular contacts, and hydrogen bonds further supports the potential for these AMPs to disrupt receptor-mediated infection pathways. While the results from this \u003cem\u003ein silico\u003c/em\u003e study highlight the promise of these AMPs, it is crucial to note that further experimental validation, including \u003cem\u003ein vitro\u003c/em\u003e and preclinical studies, is needed to establish their therapeutic efficacy. Future research should aim to translate these findings into practical applications for preventing and treating CVDs associated with infection and inflammation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSelection and Preparation of the Receptors Associated with Infection-related CVDs\u003c/h2\u003e \u003cp\u003eIn this study, five key receptors associated with infection-related CVDs were selected for analysis: Angiotensin-Converting Enzyme 2 (ACE2), C-reactive protein (CRP), Matrix Metalloproteinase-9 (MMP9), NLRP3 Inflammasome, and Toll-Like Receptor-4 (TLR4) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). To ensure accurate molecular docking and dynamics simulations, these proteins were chosen based on their well-characterized functions and the availability of high-resolution 3D structures in the Protein Data Bank (PDB). The preparation process involved retrieving the structural data, optimizing the protein structures, and refining them using Swiss-PdbViewer\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e to correct potential issues and enhance model accuracy. This refinement process included adjusting side-chain conformations, adding missing residues, and validating structural quality, ensuring the proteins were suitable for precise interaction modeling. Additionally, their active sites were defined using CASTp 3.0\u003csup\u003e64\u003c/sup\u003e, facilitating detailed docking studies with the selected antimicrobial peptides.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of target receptors related to infection-related cardiovascular diseases. It includes the Protein Data Bank (PDB) IDs for each receptor and details their respective active sites, which are critical for understanding potential interactions with antimicrobial peptides (AMPs).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceptors Associated with Infection-related CVDs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive Site\u003c/p\u003e \u003cp\u003e(Number of Residues)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngiotensin-Converting Enzyme 2 (ACE2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6M0J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24, 30, 35, 38, 41, 42, 83, 353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (CRP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1B09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58, 60, 61, 66, 74, 81, 138, 139, 140, 147, 150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatrix Metalloproteinase-9 (MMP9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1GKC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131, 149, 165, 175, 177, 182, 183, 185, 186, 187, 188, 189, 190, 197, 199, 201, 203, 205, 206, 208, 211 ,212, 213, 214, 215, 393, 401, 402, 405, 421, 422, 423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3 Inflammasome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6NPY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165, 166, 167, 229, 230, 231, 232, 379, 411, 414, 520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToll-Like Receptor-4 (TLR4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3FXI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363, 364, 365, 386, 409, 410, 411, 433, 458, 507, 533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSelection and Preparation of Antimicrobial Peptides (AMPs)\u003c/h2\u003e \u003cp\u003eSpecific criteria were employed to guide the selection of AMPs for this study, ensuring the relevance and reliability of the molecular docking and dynamics simulations. Only peptides with available 3D structures in the PDB were considered, guaranteeing the availability of accurate structural data necessary for effective modeling and simulation. The peptides chosen were classified under the SCOP (Structural Classification of Proteins) as \"peptides.\" The resolution of the structural data was restricted to 0.5\u0026ndash;2.5 \u0026Aring;, as high-resolution structures are preferred for their detailed and accurate depiction of peptide conformation, which is crucial for precise docking simulations. The binding regions of the AMPs were analyzed using CASTp 3.0\u003csup\u003e64\u003c/sup\u003e. The complete dataset, including type, PDB ID, sequence, and active residues of the selected AMPs, is provided in Supplementary Data 3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected antimicrobial peptides (AMPs) based on the employed criterion. It includes their respective PDB IDs, molecular sizes (kDa), and binding regions, which are essential for assessing their binding capabilities and therapeutic relevance in infection-related CVDs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntimicrobial Peptide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSize (kDa)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinding Regions\u003c/p\u003e \u003cp\u003e(Position of Residues)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAurein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1VM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 2, 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-defensin 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1FD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6, 7, 9, 10, 11, 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombinin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2AP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 3, 4, 6, 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCathelicidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2K6O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 4, 5, 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCecropin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1D9J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7, 9, 10, 11, 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChim2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8EB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10, 11, 14, 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDermcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2NDK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18, 19, 22, 25, 26, 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsculentin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5XDJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2, 3, 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExendin-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3C59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26, 27, 28, 29, 32, 33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepcidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3H0T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13, 14, 16, 18, 19, 20, 21, 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6VLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5, 8, 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndolicidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1HR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9, 10, 11, 12, 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactoferrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1LFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 23, 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLavracin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2N8D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 3, 4, 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagainin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2MAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 2, 6, 9, 17, 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelittin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2MLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13, 16, 17, 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicrocin J25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4CU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9, 10, 19, 20, 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1WCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9, 12, 17, 19, 20, 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePardaxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2KNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2, 3, 5, 6, 9, 15, 22, 23, 26, 27, 29, 30, 33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePiscidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6PEZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3, 4, 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleurocidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2LS9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10, 13, 14, 17, 20, 23, 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyphemusin I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1RKK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7, 9, 12, 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtegrin-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1PG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5, 6, 7, 14, 15, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePvHCt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2N1C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14, 15, 16, 17, 18, 19, 20, 22, 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtilisin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1PXQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 4, 5, 7, 9, 10, 24, 25, 29, 30, 33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTachyplesin-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2RTV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 2, 3, 16, 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTachystatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1CIX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5, 11, 12, 15, 18, 22, 23, 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporin-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6GS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4, 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThanatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8TFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11, 13, 16, 17, 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThermolysin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6FHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258, 263, 267, 305, 306, 309, 310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on these criteria, a set of 30 AMPs was chosen (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), including Exendin-4, an AMP with similarities to GLP-1, and has been investigated for its potential to influence ACE2 activity \u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. CRP is a marker of inflammation often elevated in cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. Lactoferrin, an iron-binding glycoprotein known for its antimicrobial properties, has been shown to affect CRP levels\u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e, thus, Lactoferrin potentially influences cardiovascular outcomes and highlights its role in controlling inflammation in cardiovascular diseases. Cathelicidin, another well-studied AMP, has been shown to modulate the NLRP3 inflammasome, a critical component of the inflammatory response linked to cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. These examples illustrate how AMPs interact with key receptors involved in cardiovascular diseases, offering valuable insights into their potential therapeutic applications for modulating receptor activity and managing disease progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Docking Simulations\u003c/h2\u003e \u003cp\u003eIn this phase, molecular docking simulations were meticulously performed to investigate the interactions between a selected set of AMPs and specific receptors implicated in cardiovascular diseases. These receptors are critical targets in cardiovascular diseases and infections, influencing disease progression and inflammatory responses. The molecular docking simulations aimed to investigate the interactions between the selected AMPs and CVD-related receptors associated with infection and inflammation pathways. Key interaction parameters were analyzed to understand better how AMPs bind to these target receptors. The docking results included an evaluation of the HADDOCK score, free binding energy (kcal/mol), van der Waals energy, electrostatic energy, and desolvation energy for various AMP-receptor combinations. The simulations were executed using the stand-alone version of HADDOCK (High Ambiguity Driven protein-protein DOCKing)\u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e, a robust and versatile docking software that enables detailed exploration of binding modes and energetic interactions between complex biomolecules. HADDOCK is well-regarded for incorporating experimental data into the docking process, allowing for a more accurate prediction of the possible binding conformations between AMPs and target receptors. The DX600 peptide (sequence: GDYSHCSPLRYYPWWKCTYPDPEGGG) was used as a reference standard due to its known interaction with CVD-related receptors like ACE2 (IC\u003csub\u003e50\u003c/sub\u003e: 10.1 \u0026micro;M) and therapeutic implications in cardiovascular conditions\u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e. The 3D structure of peptide 35409 was generated using AlphaFold\u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e, providing a precise model for further analysis. DX600 peptide was utilized as a benchmark to evaluate the efficacy of other AMPs in binding to the target receptors. Using this standard peptide allowed for a comparative analysis of the binding efficiency and inhibitory potential of other AMPs. The insights gained from these simulations are intended to guide the selection of AMPs with the highest potential for therapeutic applications. To further refine and validate the docking results, PRODIGY (PROtein binDIng enerGY prediction) \u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e was employed to predict the binding affinity of the AMP-adhesion protein complexes. PRODIGY is an advanced computational tool that leverages state-of-the-art algorithms to estimate the binding affinity between interacting proteins and ligands based on structural data \u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e. This prediction of binding free energy is critical for ranking the AMP candidates, as it provides a quantitative measure of how strongly an AMP binds to a target receptor. By identifying the AMPs with the most favorable binding affinities, PRODIGY helps narrow down the candidates most likely to impact cardiovascular disease receptors effectively. All molecular docking simulations were conducted on a high-performance computing workstation with an Intel\u0026reg; Core\u0026trade; i7-12650H processor, an NVIDIA\u0026trade; RTX 4060 graphics card with 8 GB VRAM, and 16 GB of DDR5 RAM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Dynamics (MD) Simulations\u003c/h2\u003e \u003cp\u003eMolecular dynamics (MD) simulations were utilized to explore the dynamics and stability of AMP-receptor complexes, specifically focusing on interactions between AMPs and receptors implicated in infection-related CVDs. The simulations were conducted using GROMACS 2022.5\u003csup\u003e109\u003c/sup\u003e, a highly regarded tool known for its accuracy and efficiency in modeling biomolecular systems. The Optimized Potentials for Liquid Simulations (OPLS-AA/L) force field was employed to accurately represent molecular interactions within these complexes \u003csup\u003e\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e,\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e. Simulation boxes were configured using default cubic box parameters to accommodate the AMP-receptor complexes effectively. Standard procedures were followed, including adding water molecules via the Single Point Charge Extended (SPCE) model and incorporating counterions to ensure system neutrality\u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. Energy minimization was conducted using the steepest-descent method to eliminate steric clashes and stabilize the system. Equilibration was performed in two stages: first, in the number of particles, volume, and temperature (NVT) ensemble to stabilize temperature and system conditions, and second, in the number of particles, pressure, and temperature (NPT) ensemble to maintain constant pressure and temperature\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e. Following equilibration, production MD simulations were conducted for 100 nanoseconds to observe the long-term dynamics of the AMP-receptor complexes. During the simulations, parameters such as Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (RoG), potential energies, and intermolecular hydrogen bonding interactions were monitored and analyzed to evaluate the stability and conformational dynamics of the complexes. Molecular visualization software, including PyMOL \u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e and UCSF Chimera \u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e, was used to visualize critical residues and intermolecular interactions within the simulated complexes. This analysis provided valuable insights into the binding mechanisms and stability of the AMPs with the cardiovascular disease receptors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Mechanics/Poisson\u0026ndash;Boltzmann Surface Area (MM/PBSA) Calculations\u003c/h2\u003e \u003cp\u003eThe Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) method evaluated the peptide-receptor interactions involving AMPs and receptors implicated in infection-related CVDs. This method utilizes MD simulation data to calculate the binding free energy of the AMP-receptor complexes, providing insights into the strength and stability of these interactions. The MD simulations generated a variety of receptor conformations, and representative snapshots from these simulations were selected for detailed analysis \u003csup\u003e\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e. Comprehensive energy computations were performed for each snapshot, including gas-phase energy calculations, solvation energy estimation using a continuum solvent model, and entropy calculations. These components were integrated to determine the overall binding free energy of the AMP-receptor complex\u003csup\u003e\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e,\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e. The calculations used the \u003cem\u003egmx_MMPBSA\u003c/em\u003e module available within the GROMACS simulation package\u003csup\u003e\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e,\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e\u003c/sup\u003e. It is well-regarded for its precision and efficiency in calculating binding free energies for biomolecular complexes. The MM/PBSA method is precious for predicting binding affinities, as it accounts for the energetic and solvation contributions to the binding process\u003csup\u003e\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u003c/sup\u003e. The binding free energy (ΔG_binding) was computed using the following equation:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eΔG_binding\u0026thinsp;=\u0026thinsp;ΔG_complex - ΔG_peptide - ΔG_protein\u003c/h2\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eΔG_binding: the binding free energy associated with forming the peptide-protein complex.\u003c/p\u003e \u003cp\u003eΔG_complex: the free energy of the fully solvated peptide-protein complex.\u003c/p\u003e \u003cp\u003eΔG_peptide: the free energy of peptide in its solvated state when unbound.\u003c/p\u003e \u003cp\u003eΔG_protein: the free energy of protein in its solvated state when unbound.\u003c/p\u003e \u003cp\u003eBy calculating the difference between the free energy of the complex and the combined free energies of the unbound AMP and receptor, this method provided insights into the energetic changes that occur upon complex formation, elucidating the interaction's strength and stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eHaemolytic Activity Prediction of Antimicrobial Peptides (AMPs)\u003c/h2\u003e \u003cp\u003eThe haemolytic activity of selected AMPs was evaluated using the Hemolytic Activity Prediction for Peptides and Proteins (HAPPENN) tool. HAPPENN is a novel computational tool designed specifically for predicting the hemolytic potential of therapeutic peptides by employing advanced neural network algorithms\u003csup\u003e\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e\u003c/sup\u003e. The amino acid sequences of the selected AMPs were input into the HAPPENN platform to conduct the haemolytic activity assessment. This tool utilizes a dataset of known peptides with established haemolytic activity to train its neural network. The training process involves analyzing the structural and compositional features of the peptides, such as hydrophobicity, charge distribution, and amino acid sequences, which are critical in determining their interaction with erythrocyte membranes. Once the input sequences were processed, HAPPENN generated predictions regarding the haemolytic potential of each peptide. The output includes a quantitative score that reflects the likelihood of a given AMP to induce hemolysis in red blood cells, allowing for a comparative assessment of the AMPs' safety profiles.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eACE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eAngiotensin-converting enzyme 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eAMPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eAntimicrobial peptides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eC-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eCVDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eCardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eHAPPENN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eHemolytic activity prediction for peptides and proteins\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eIntermolecular contact\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eMolecular dynamics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eMDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eMultidrug-resistant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eMM/PBSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eMolecular Mechanics/Poisson\u0026ndash;Boltzmann Surface Area\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eMMPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eMatrix metalloproteinases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eNIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eNon-interacting surface\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eOPLS-AA/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eOptimized potentials for liquid simulations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003ePAMPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003ePathogen-associated molecular patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eRMSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eRoot mean square deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eRMSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eRoot mean square fluctuation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eRoG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eRadius of gyration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eSCOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eStructural classification of proteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eSPCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eSingle point charge extended\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.774%;\"\u003e\n \u003cp\u003eTLRs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.226%;\"\u003e\n \u003cp\u003eToll-like receptors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-RG23154).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eD.D. and N.A. contributed to the conception, design and supervision of the work and revision of the manuscript. D.D. contributed to the acquisition and analysis of the computational data/results. D.D. and N.A. wrote the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen, L. et al. Inflammatory responses and inflammation-associated diseases in organs. \u003cem\u003eOncotarget\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, 7204\u0026ndash;7218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/oncotarget.23208\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.23208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, T. H. et al. A Review on The Pathogenesis of Cardiovascular Disease of Flaviviridea Viruses Infection. \u003cem\u003eViruses\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 365 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, C. \u0026amp; Waters, D. D. Chlamydia pneumoniae and atherosclerosis: from Koch postulates to clinical trials. \u003cem\u003eProg Cardiovasc. Dis.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, 230\u0026ndash;239. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pcad.2005.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.pcad.2005.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung, S. H. \u0026amp; Lee, K. T. Atherosclerosis by Virus Infection-A Short Review. \u003cem\u003eBiomedicines\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biomedicines10102634\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines10102634\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolf, D. \u0026amp; Ley, K. Immunity and Inflammation in Atherosclerosis. \u003cem\u003eCirc. Res.\u003c/em\u003e \u003cb\u003e124\u003c/b\u003e, 315\u0026ndash;327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circresaha.118.313591\u003c/span\u003e\u003cspan address=\"10.1161/circresaha.118.313591\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaera, N. et al. Impact of Immunity on Coronary Artery Disease: An Updated Pathogenic Interplay and Potential Therapeutic Strategies. \u003cem\u003eLife (Basel)\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/life13112128\u003c/span\u003e\u003cspan address=\"10.3390/life13112128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJa\u0026eacute;n, R. I. et al. Innate Immune Receptors, Key Actors in Cardiovascular Diseases. \u003cem\u003eJACC: Basic. Translational Sci.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 735\u0026ndash;749. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacbts.2020.03.015\u003c/span\u003e\u003cspan address=\"10.1016/j.jacbts.2020.03.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKircheis, R. \u0026amp; Planz, O. The Role of Toll-like Receptors (TLRs) and Their Related Signaling Pathways in Viral Infection and Inflammation. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms24076701\u003c/span\u003e\u003cspan address=\"10.3390/ijms24076701\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoulopoulou, S., McCarthy, C. G. \u0026amp; Webb, R. C. Toll-like Receptors in the Vascular System: Sensing the Dangers Within. \u003cem\u003ePharmacol. Rev.\u003c/em\u003e \u003cb\u003e68\u003c/b\u003e, 142\u0026ndash;167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1124/pr.114.010090\u003c/span\u003e\u003cspan address=\"10.1124/pr.114.010090\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanase, D. M. et al. Portrayal of NLRP3 Inflammasome in Atherosclerosis: Current Knowledge and Therapeutic Targets. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms24098162\u003c/span\u003e\u003cspan address=\"10.3390/ijms24098162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarasawa, T. \u0026amp; Takahashi, M. Role of NLRP3 Inflammasomes in Atherosclerosis. \u003cem\u003eJ. Atheroscler Thromb.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 443\u0026ndash;451. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5551/jat.RV17001\u003c/span\u003e\u003cspan address=\"10.5551/jat.RV17001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBourgonje, A. R. et al. Angiotensin-converting enzyme 2 (ACE2), SARS-CoV-2 and the pathophysiology of coronavirus disease 2019 (COVID-19). \u003cem\u003eJ. Pathol.\u003c/em\u003e \u003cb\u003e251\u003c/b\u003e, 228\u0026ndash;248. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/path.5471\u003c/span\u003e\u003cspan address=\"10.1002/path.5471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, L. P., Zhang, X. L. \u0026amp; Li, J. New perspectives on angiotensin-converting enzyme 2 and its related diseases. \u003cem\u003eWorld J. Diabetes\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 839\u0026ndash;854. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4239/wjd.v12.i6.839\u003c/span\u003e\u003cspan address=\"10.4239/wjd.v12.i6.839\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao, Q. et al. Impact of Cardiovascular Diseases on COVID-19: A Systematic Review. \u003cem\u003eMed. Sci. Monit.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, e930032. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12659/msm.930032\u003c/span\u003e\u003cspan address=\"10.12659/msm.930032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlejarz, W., Łacheta, D. \u0026amp; Kubiak-Tomaszewska, G. Matrix Metalloproteinases as Biomarkers of Atherosclerotic Plaque Instability. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms21113946\u003c/span\u003e\u003cspan address=\"10.3390/ijms21113946\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, P., Sun, M. \u0026amp; Sader, S. Matrix metalloproteinases in cardiovascular disease. \u003cem\u003eCan J Cardiol\u003c/em\u003e 22 Suppl B, 25b-30b, doi: (2006). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0828-282x(06)70983-7\u003c/span\u003e\u003cspan address=\"10.1016/s0828-282x(06)70983-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorritt, R. A. \u0026amp; Crother, T. R. Chlamydia pneumoniae Infection and Inflammatory Diseases. \u003cem\u003eImmunopathol. Dis. Th.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 237\u0026ndash;254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1615/ForumImmunDisTher.2017020161\u003c/span\u003e\u003cspan address=\"10.1615/ForumImmunDisTher.2017020161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhlestein, J. B. et al. Infection With Chlamydia pneumoniae Accelerates the Development of Atherosclerosis and Treatment With Azithromycin Prevents It in a Rabbit Model. \u003cem\u003eCirculation\u003c/em\u003e. \u003cb\u003e97\u003c/b\u003e, 633\u0026ndash;636. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.CIR.97.7.633\u003c/span\u003e\u003cspan address=\"10.1161/01.CIR.97.7.633\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlor, C. \u0026amp; Bjerrum, L. Antimicrobial resistance: risk associated with antibiotic overuse and initiatives to reduce the problem. \u003cem\u003eTher. Adv. Drug Saf.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 229\u0026ndash;241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/2042098614554919\u003c/span\u003e\u003cspan address=\"10.1177/2042098614554919\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuteeb, G., Rehman, M. T., Shahwan, M. \u0026amp; Aatif, M. Origin of Antibiotics and Antibiotic Resistance, and Their Impacts on Drug Development: A Narrative Review. \u003cem\u003ePharmaceuticals (Basel)\u003c/em\u003e. 16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ph16111615\u003c/span\u003e\u003cspan address=\"10.3390/ph16111615\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed, S. K. et al. Antimicrobial resistance: Impacts, challenges, and future prospects. \u003cem\u003eJ. Med. Surg. Public. Health\u003c/em\u003e. \u003cb\u003e2\u003c/b\u003e, 100081. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.glmedi.2024.100081\u003c/span\u003e\u003cspan address=\"10.1016/j.glmedi.2024.100081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeianza, Y. et al. Duration and life-stage of antibiotic use and risk of cardiovascular events in women. \u003cem\u003eEur. Heart J.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 3838\u0026ndash;3845. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehz231\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehz231\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuong, H. X., Thanh, T. T. \u0026amp; Tran, T. H. Antimicrobial peptides - Advances in development of therapeutic applications. \u003cem\u003eLife Sci.\u003c/em\u003e \u003cb\u003e260\u003c/b\u003e, 118407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.lfs.2020.118407\u003c/span\u003e\u003cspan address=\"10.1016/j.lfs.2020.118407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXuan, J. et al. Antimicrobial peptides for combating drug-resistant bacterial infections. \u003cem\u003eDrug Resist. Updates\u003c/em\u003e. \u003cb\u003e68\u003c/b\u003e, 100954. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.drup.2023.100954\u003c/span\u003e\u003cspan address=\"10.1016/j.drup.2023.100954\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenfield, A. H. \u0026amp; Henriques, S. T. Mode-of-Action of Antimicrobial Peptides: Membrane Disruption vs. Intracellular Mechanisms. \u003cem\u003eFront. Med. Technol.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 610997. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmedt.2020.610997\u003c/span\u003e\u003cspan address=\"10.3389/fmedt.2020.610997\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong, H. et al. How do antimicrobial peptides disrupt the lipopolysaccharide membrane leaflet of Gram-negative bacteria? \u003cem\u003eJ. Colloid Interface Sci.\u003c/em\u003e \u003cb\u003e637\u003c/b\u003e, 182\u0026ndash;192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jcis.2023.01.051\u003c/span\u003e\u003cspan address=\"10.1016/j.jcis.2023.01.051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Q. Y. et al. Antimicrobial peptides: mechanism of action, activity and clinical potential. \u003cem\u003eMilitary Med. Res.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40779-021-00343-2\u003c/span\u003e\u003cspan address=\"10.1186/s40779-021-00343-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDermawan, D., Sumirtanurdin, R. \u0026amp; Dewantisari, D. Simulasi dinamika molekular reseptor estrogen alfa dengan andrografolid sebagai anti kanker payudara. \u003cem\u003eIndones J. Pharm. Sci. Technol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 65\u0026ndash;76 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNnyigide, O. S., Lee, S. G. \u0026amp; Hyun, K. Silico Characterization of the Binding Modes of Surfactants with Bovine Serum Albumin. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 10643. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-019-47135-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-47135-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZasloff, M. Antimicrobial peptides of multicellular organisms. \u003cem\u003eNature\u003c/em\u003e. \u003cb\u003e415\u003c/b\u003e, 389\u0026ndash;395. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/415389a\u003c/span\u003e\u003cspan address=\"10.1038/415389a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanz, T. Defensins: antimicrobial peptides of innate immunity. \u003cem\u003eNat. Rev. Immunol.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 710\u0026ndash;720. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nri1180\u003c/span\u003e\u003cspan address=\"10.1038/nri1180\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFesta, M. et al. Cardiovascular Active Peptides of Marine Origin with ACE Inhibitory Activities: Potential Role as Anti-Hypertensive Drugs and in Prevention of SARS-CoV-2 Infection. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms21218364\u003c/span\u003e\u003cspan address=\"10.3390/ijms21218364\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, R. T. Matrix metalloproteinase inhibition and the prevention of heart failure. \u003cem\u003eTrends Cardiovasc. Med.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 202\u0026ndash;205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1050-1738(01)00113-x\u003c/span\u003e\u003cspan address=\"10.1016/s1050-1738(01)00113-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNdinguri, M. W., Bhowmick, M., Tokmina-Roszyk, D., Robichaud, T. K. \u0026amp; Fields, G. B. Peptide-based selective inhibitors of matrix metalloproteinase-mediated activities. \u003cem\u003eMolecules\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e, 14230\u0026ndash;14248. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/molecules171214230\u003c/span\u003e\u003cspan address=\"10.3390/molecules171214230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Musaimi, O., Lombardi, L., Williams, D. R. \u0026amp; Albericio, F. Strategies for Improving Peptide Stability and Delivery. \u003cem\u003ePharmaceuticals (Basel)\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ph15101283\u003c/span\u003e\u003cspan address=\"10.3390/ph15101283\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain, M. S. et al. Therapeutic Potential of Antiviral Peptides against the NS2B/NS3 Protease of Zika Virus. \u003cem\u003eACS Omega\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 35207\u0026ndash;35218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acsomega.3c04903\u003c/span\u003e\u003cspan address=\"10.1021/acsomega.3c04903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDermawan, D., Bahtiar, R. \u0026amp; Sofian, F. F. Implementation of Green Supply Chain Management (GSCM) in the pharmaceutical industry in Indonesia: feasibility analysis and case studies. \u003cem\u003eJ. Ilm Farm.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 23\u0026ndash;29 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKastritis, P. L. \u0026amp; Bonvin, A. M. On the binding affinity of macromolecular interactions: daring to ask why proteins interact. \u003cem\u003eJ. R Soc. Interface\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e, 20120835. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rsif.2012.0835\u003c/span\u003e\u003cspan address=\"10.1098/rsif.2012.0835\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, D. et al. Improved functionality and safety of peptides by the formation of peptide-polyphenol complexes. \u003cem\u003eTrends Food Sci. Technol.\u003c/em\u003e \u003cb\u003e141\u003c/b\u003e, 104193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tifs.2023.104193\u003c/span\u003e\u003cspan address=\"10.1016/j.tifs.2023.104193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobles-Loaiza, A. A. et al. Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity. \u003cem\u003ePharmaceuticals (Basel)\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ph15030323\u003c/span\u003e\u003cspan address=\"10.3390/ph15030323\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmorim, A. M. B. et al. Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction. \u003cem\u003eChem. Res. Toxicol.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 827\u0026ndash;849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.chemrestox.3c00352\u003c/span\u003e\u003cspan address=\"10.1021/acs.chemrestox.3c00352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAoki, W. \u0026amp; Ueda, M. Characterization of Antimicrobial Peptides toward the Development of Novel Antibiotics. \u003cem\u003ePharmaceuticals (Basel)\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e, 1055\u0026ndash;1081. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ph6081055\u003c/span\u003e\u003cspan address=\"10.3390/ph6081055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidtchen, A., Pasupuleti, M. \u0026amp; Malmsten, M. Effect of hydrophobic modifications in antimicrobial peptides. \u003cem\u003eAdv. Colloid Interface Sci.\u003c/em\u003e \u003cb\u003e205\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cis.2013.06.009\u003c/span\u003e\u003cspan address=\"10.1016/j.cis.2013.06.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, X. et al. Characterization of antimicrobial activity and mechanisms of low amphipathic peptides with different α-helical propensity. \u003cem\u003eActa Biomater.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 155\u0026ndash;167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.actbio.2015.02.023\u003c/span\u003e\u003cspan address=\"10.1016/j.actbio.2015.02.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Q. Y. et al. Antimicrobial peptides: mechanism of action, activity and clinical potential. \u003cem\u003eMil Med. Res.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40779-021-00343-2\u003c/span\u003e\u003cspan address=\"10.1186/s40779-021-00343-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRotem, S., Radzishevsky, I. \u0026amp; Mor, A. Physicochemical properties that enhance discriminative antibacterial activity of short dermaseptin derivatives. \u003cem\u003eAntimicrob. Agents Chemother.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 2666\u0026ndash;2672. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/aac.00030-06\u003c/span\u003e\u003cspan address=\"10.1128/aac.00030-06\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZainal Baharin, N. H. et al. The characteristics and roles of antimicrobial peptides as potential treatment for antibiotic-resistant pathogens: a review. \u003cem\u003ePeerJ\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, e12193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7717/peerj.12193\u003c/span\u003e\u003cspan address=\"10.7717/peerj.12193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmatuni, A., Shuster, A., Abegg, D., Adibekian, A. \u0026amp; Renata, H. Comprehensive Structure-Activity Relationship Studies of Cepafungin Enabled by Biocatalytic C-H Oxidations. \u003cem\u003eACS Cent. Sci.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 239\u0026ndash;251. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acscentsci.2c01219\u003c/span\u003e\u003cspan address=\"10.1021/acscentsci.2c01219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuha, R. On Exploring Structure\u0026ndash;Activity Relationships. \u003cem\u003eMethods in molecular biology\u003c/em\u003e. \u003cem\u003e(Clifton N J)\u003c/em\u003e. \u003cb\u003e993\u003c/b\u003e, 81\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-1-62703-342-8_6\u003c/span\u003e\u003cspan address=\"10.1007/978-1-62703-342-8_6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan, Z. et al. Sustained Release of a Peptide-Based Matrix Metalloproteinase-2 Inhibitor to Attenuate Adverse Cardiac Remodeling and Improve Cardiac Function Following Myocardial Infarction. \u003cem\u003eBiomacromolecules\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 2820\u0026ndash;2829. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.biomac.7b00760\u003c/span\u003e\u003cspan address=\"10.1021/acs.biomac.7b00760\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans, B. J., King, A. T., Katsifis, A., Matesic, L. \u0026amp; Jamie, J. F. Methods to Enhance the Metabolic Stability of Peptide-Based PET Radiopharmaceuticals. \u003cem\u003eMolecules\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/molecules25102314\u003c/span\u003e\u003cspan address=\"10.3390/molecules25102314\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026ouml;ttger, R., Hoffmann, R. \u0026amp; Knappe, D. Differential stability of therapeutic peptides with different proteolytic cleavage sites in blood, plasma and serum. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, e0178943. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0178943\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0178943\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyan, J. T., Ross, R. P., Bolton, D., Fitzgerald, G. F. \u0026amp; Stanton, C. Bioactive peptides from muscle sources: meat and fish. \u003cem\u003eNutrients\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e, 765\u0026ndash;791. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu3090765\u003c/span\u003e\u003cspan address=\"10.3390/nu3090765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMusliha, A., Dermawan, D., Rahayu, P. \u0026amp; Tjandrawinata, R. R. Unraveling modulation effects on albumin synthesis and inflammation by Striatin, a bioactive protein fraction isolated from \u0026lt;\u0026thinsp;em\u0026thinsp;\u0026gt;\u0026thinsp;Channa striata: \u0026thinsp;\u003cem\u003eIn silico\u0026thinsp;proteomics and \u0026lt;\u0026thinsp;em\u0026thinsp;\u0026gt;\u0026thinsp;in vitro\u0026thinsp;approaches. Heliyon 10\u003c/em\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2024.e38386\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e38386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(2024).\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan, Y., Zhang, M., Lai, R. \u0026amp; Zhang, Z. Chemical modifications to increase the therapeutic potential of antimicrobial peptides. \u003cem\u003ePeptides\u003c/em\u003e. \u003cb\u003e146\u003c/b\u003e, 170666. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.peptides.2021.170666\u003c/span\u003e\u003cspan address=\"10.1016/j.peptides.2021.170666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing, Y. et al. Impact of non-proteinogenic amino acids in the discovery and development of peptide therapeutics. \u003cem\u003eAmino Acids\u003c/em\u003e. \u003cb\u003e52\u003c/b\u003e, 1207\u0026ndash;1226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00726-020-02890-9\u003c/span\u003e\u003cspan address=\"10.1007/s00726-020-02890-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavallaro, P. A. et al. Peptides Targeting HER2-Positive Breast Cancer Cells and Applications in Tumor Imaging and Delivery of Chemotherapeutics. \u003cem\u003eNanomaterials (Basel)\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nano13172476\u003c/span\u003e\u003cspan address=\"10.3390/nano13172476\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhn, W. Y. \u0026amp; Busemeyer, J. R. Challenges and promises for translating computational tools into clinical practice. \u003cem\u003eCurr. Opin. Behav. Sci.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cobeha.2016.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.cobeha.2016.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahadik, R., Kiptoo, P., Tolbert, T. \u0026amp; Siahaan, T. J. Immune Modulation by Antigenic Peptides and Antigenic Peptide Conjugates for Treatment of Multiple Sclerosis. \u003cem\u003eMed. Res. Arch.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18103/mra.v10i5.2804\u003c/span\u003e\u003cspan address=\"10.18103/mra.v10i5.2804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJawa, V. et al. T-Cell Dependent Immunogenicity of Protein Therapeutics Pre-clinical Assessment and Mitigation-Updated Consensus and Review 2020. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1301. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2020.01301\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2020.01301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossino, G. et al. Peptides as Therapeutic Agents: Challenges and Opportunities in the Green Transition Era. \u003cem\u003eMolecules\u003c/em\u003e. \u003cb\u003e28\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/molecules28207165\u003c/span\u003e\u003cspan address=\"10.3390/molecules28207165\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, L. et al. Therapeutic peptides: current applications and future directions. \u003cem\u003eSignal. Transduct. Target. Therapy\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, 48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41392-022-00904-4\u003c/span\u003e\u003cspan address=\"10.1038/s41392-022-00904-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuex, N. \u0026amp; Peitsch, M. C. SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. \u003cem\u003eElectrophoresis\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 2714\u0026ndash;2723. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/elps.1150181505\u003c/span\u003e\u003cspan address=\"10.1002/elps.1150181505\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian, W., Chen, C., Lei, X., Zhao, J. \u0026amp; Liang, J. CASTp 3.0: computed atlas of surface topography of proteins. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e, 363\u0026ndash;367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gky473\u003c/span\u003e\u003cspan address=\"10.1093/nar/gky473\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLan, J. et al. Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. \u003cem\u003eNature\u003c/em\u003e. \u003cb\u003e581\u003c/b\u003e, 215\u0026ndash;220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-020-2180-5\u003c/span\u003e\u003cspan address=\"10.1038/s41586-020-2180-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson, D., Pepys, M. B. \u0026amp; Wood, S. P. The physiological structure of human C-reactive protein and its complex with phosphocholine. \u003cem\u003eStructure\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, 169\u0026ndash;177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0969-2126(99)80023-9\u003c/span\u003e\u003cspan address=\"10.1016/S0969-2126(99)80023-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRowsell, S. et al. Crystal Structure of Human MMP9 in Complex with a Reverse Hydroxamate Inhibitor. \u003cem\u003eJ. Mol. Biol.\u003c/em\u003e \u003cb\u003e319\u003c/b\u003e, 173\u0026ndash;181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0022-2836(02)00262-0\u003c/span\u003e\u003cspan address=\"10.1016/S0022-2836(02)00262-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharif, H. et al. Structural mechanism for NEK7-licensed activation of NLRP3 inflammasome. \u003cem\u003eNature\u003c/em\u003e. \u003cb\u003e570\u003c/b\u003e, 338\u0026ndash;343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-019-1295-z\u003c/span\u003e\u003cspan address=\"10.1038/s41586-019-1295-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark, B. S. et al. The structural basis of lipopolysaccharide recognition by the TLR4\u0026ndash;MD-2 complex. \u003cem\u003eNature\u003c/em\u003e. \u003cb\u003e458\u003c/b\u003e, 1191\u0026ndash;1195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature07830\u003c/span\u003e\u003cspan address=\"10.1038/nature07830\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, G., Li, Y. \u0026amp; Li, X. Correlation of Three-dimensional Structures with the Antibacterial Activity of a Group of Peptides Designed Based on a Nontoxic Bacterial Membrane Anchor *. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e280\u003c/b\u003e, 5803\u0026ndash;5811. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M410116200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M410116200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eet al. The Structure of Human β-Defensin-2 Shows Evidence of Higher Order Oligomerization*. \u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e 275, 32911\u0026ndash;32918, doi:10.1074/jbc.M006098200 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZangger, K., G\u0026ouml;\u0026szlig;ler, R., Khatai, L., Lohner, K. \u0026amp; Jilek, A. Structures of the glycine-rich diastereomeric peptides bombinin H2 and H4. \u003cem\u003eToxicon\u003c/em\u003e 52, 246\u0026ndash;254, doi: (2008). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.toxicon.2008.05.011\u003c/span\u003e\u003cspan address=\"10.1016/j.toxicon.2008.05.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, G. Structures of Human Host Defense Cathelicidin LL-37 and Its Smallest Antimicrobial Peptide KR-12 in Lipid Micelles *. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e283\u003c/b\u003e, 32637\u0026ndash;32643. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M805533200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M805533200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOh, D. et al. NMR structural characterization of cecropin A(1\u0026ndash;8) - magainin 2(1\u0026ndash;12) and cecropin A (1\u0026ndash;8) - melittin (1\u0026ndash;12) hybrid peptides. \u003cem\u003eJ. Pept. Res.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 578\u0026ndash;589. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1034/j.1399-3011.1999.00067.x\u003c/span\u003e\u003cspan address=\"10.1034/j.1399-3011.1999.00067.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Viana, T. et al. Release of immunomodulatory peptides at bacterial membrane interfaces as a novel strategy to fight microorganisms. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e299\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jbc.2023.103056\u003c/span\u003e\u003cspan address=\"10.1016/j.jbc.2023.103056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen, V. S., Tan, K. W., Ramesh, K., Chew, F. T. \u0026amp; Mok, Y. K. Structural basis for the bacterial membrane insertion of dermcidin peptide, DCD-1L. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 13923. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-017-13600-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-017-13600-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoffredo, M. R. et al. Membrane perturbing activities and structural properties of the frog-skin derived peptide Esculentin-1a(1\u0026ndash;21)NH2 and its Diastereomer Esc(1\u0026ndash;21)-1c: Correlation with their antipseudomonal and cytotoxic activity. \u003cem\u003eBiochim. et Biophys. Acta (BBA) - Biomembr.\u003c/em\u003e \u003cb\u003e1859\u003c/b\u003e, 2327\u0026ndash;2339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbamem.2017.09.009\u003c/span\u003e\u003cspan address=\"10.1016/j.bbamem.2017.09.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRunge, S., Th\u0026oslash;gersen, H., Madsen, K., Lau, J. \u0026amp; Rudolph, R. Crystal Structure of the Ligand-bound Glucagon-like Peptide-1 Receptor Extracellular Domain *. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e283\u003c/b\u003e, 11340\u0026ndash;11347. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M708740200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M708740200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJordan, J. B. et al. Hepcidin Revisited, Disulfide Connectivity, Dynamics, and Structure\u0026thinsp;\u0026lt;\u0026thinsp;sup\u0026gt;. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e284\u003c/b\u003e, 24155\u0026ndash;24167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M109.017764\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M109.017764\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMariano, G. H. et al. Characterization of novel human intragenic antimicrobial peptides, incorporation and release studies from ureasil-polyether hybrid matrix. \u003cem\u003eMater. Sci. Engineering: C\u003c/em\u003e. \u003cb\u003e119\u003c/b\u003e, 111581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.msec.2020.111581\u003c/span\u003e\u003cspan address=\"10.1016/j.msec.2020.111581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedrich, C. L., Rozek, A., Patrzykat, A. \u0026amp; Hancock, R. E. W. Structure and Mechanism of Action of an Indolicidin Peptide Derivative with Improved Activity against Gram-positive Bacteria *. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e276\u003c/b\u003e, 24015\u0026ndash;24022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M009691200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M009691200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang, P. M., Zhou, N., Shan, X., Arrowsmith, C. H. \u0026amp; Vogel, H. J. Three-Dimensional Solution Structure of Lactoferricin B, an Antimicrobial Peptide Derived from Bovine Lactoferrin. \u003cem\u003eBiochemistry\u003c/em\u003e. \u003cb\u003e37\u003c/b\u003e, 4288\u0026ndash;4298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/bi972323m\u003c/span\u003e\u003cspan address=\"10.1021/bi972323m\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePillong, M. et al. Rational Design of Membrane-Pore-Forming Peptides. \u003cem\u003eSmall\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 1701316. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/smll.201701316\u003c/span\u003e\u003cspan address=\"10.1002/smll.201701316\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGesell, J., Zasloff, M. \u0026amp; Opella, S. J. Two-dimensional 1H NMR experiments show that the 23-residue magainin antibiotic peptide is an α-helix in dodecylphosphocholine micelles, sodium dodecylsulfate micelles, and trifluoroethanol/water solution. \u003cem\u003eJ. Biomol. NMR.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 127\u0026ndash;135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1023/A:1018698002314\u003c/span\u003e\u003cspan address=\"10.1023/A:1018698002314\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerwilliger, T. C., Weissman, L. \u0026amp; Eisenberg, D. The structure of melittin in the form I crystals and its implication for melittin's lytic and surface activities. \u003cem\u003eBiophys. J.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 353\u0026ndash;361. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0006-3495(82)84683-3\u003c/span\u003e\u003cspan address=\"10.1016/s0006-3495(82)84683-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1982).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathavan, I. et al. Structural basis for hijacking siderophore receptors by antimicrobial lasso peptides. \u003cem\u003eNat. Chem. Biol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 340\u0026ndash;342. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nchembio.1499\u003c/span\u003e\u003cspan address=\"10.1038/nchembio.1499\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu, S. T. D. et al. The nisin\u0026ndash;lipid II complex reveals a pyrophosphate cage that provides a blueprint for novel antibiotics. \u003cem\u003eNat. Struct. Mol. Biol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 963\u0026ndash;967. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nsmb830\u003c/span\u003e\u003cspan address=\"10.1038/nsmb830\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhunia, A. et al. NMR Structure of Pardaxin, a Pore-forming Antimicrobial Peptide, in Lipopolysaccharide Micelles: MECHANISM OF OUTER MEMBRANE PERMEABILIZATION 2\u0026thinsp;\u0026lt;\u0026thinsp;sup\u0026gt;. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e285\u003c/b\u003e, 3883\u0026ndash;3895. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M109.065672\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M109.065672\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComert, F. et al. The host-defense peptide piscidin P1 reorganizes lipid domains in membranes and decreases activation energies in mechanosensitive ion channels. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e294\u003c/b\u003e, 18557\u0026ndash;18570. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.RA119.010232\u003c/span\u003e\u003cspan address=\"10.1074/jbc.RA119.010232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmos, S. T. et al. Antimicrobial Peptide Potency is Facilitated by Greater Conformational Flexibility when Binding to Gram-negative Bacterial Inner Membranes. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 37639. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/srep37639\u003c/span\u003e\u003cspan address=\"10.1038/srep37639\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePowers, J. P. S., Rozek, A. \u0026amp; Hancock, R. E. W. Structure\u0026ndash;activity relationships for the β-hairpin cationic antimicrobial peptide polyphemusin I. \u003cem\u003eBiochimica et Biophysica Acta (BBA) - Proteins and Proteomics\u003c/em\u003e 1698, 239\u0026ndash;250, doi: (2004). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbapap.2003.12.009\u003c/span\u003e\u003cspan address=\"10.1016/j.bbapap.2003.12.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFahrner, R. L. et al. Solution structure of protegrin-1, a broad-spectrum antimicrobial peptide from porcine leukocytes. \u003cem\u003eChem. Biol.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 543\u0026ndash;550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1074-5521(96)90145-3\u003c/span\u003e\u003cspan address=\"10.1016/s1074-5521(96)90145-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetit, V. W. et al. A hemocyanin-derived antimicrobial peptide from the penaeid shrimp adopts an alpha-helical structure that specifically permeabilizes fungal membranes. \u003cem\u003eBiochim. et Biophys. Acta (BBA) - Gen. Subj.\u003c/em\u003e \u003cb\u003e1860\u003c/b\u003e, 557\u0026ndash;568. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbagen.2015.12.010\u003c/span\u003e\u003cspan address=\"10.1016/j.bbagen.2015.12.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawulka, K. E. et al. Structure of Subtilosin A, a Cyclic Antimicrobial Peptide from Bacillus subtilis with Unusual Sulfur to α-Carbon Cross-Links: Formation and Reduction of α-Thio-α-Amino Acid Derivatives. \u003cem\u003eBiochemistry\u003c/em\u003e. \u003cb\u003e43\u003c/b\u003e, 3385\u0026ndash;3395. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/bi0359527\u003c/span\u003e\u003cspan address=\"10.1021/bi0359527\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKushibiki, T. et al. Interaction between tachyplesin I, an antimicrobial peptide derived from horseshoe crab, and lipopolysaccharide. \u003cem\u003eBiochim. et Biophys. Acta (BBA) - Proteins Proteom.\u003c/em\u003e \u003cb\u003e1844\u003c/b\u003e, 527\u0026ndash;534. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbapap.2013.12.017\u003c/span\u003e\u003cspan address=\"10.1016/j.bbapap.2013.12.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujitani, N. et al. Structure of the Antimicrobial Peptide Tachystatin A *. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e277\u003c/b\u003e, 23651\u0026ndash;23657. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M111120200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M111120200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManzo, G. et al. Temporin L and aurein 2.5 have identical conformations but subtly distinct membrane and antibacterial activities. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 10934. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-019-47327-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-47327-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandard, N. et al. Solution structure of thanatin, a potent bactericidal and fungicidal insect peptide, determined from proton two-dimensional nuclear magnetic resonance data. \u003cem\u003eEur. J. Biochem.\u003c/em\u003e \u003cb\u003e256\u003c/b\u003e, 404\u0026ndash;410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1432-1327.1998.2560404.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1432-1327.1998.2560404.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiebig, D. et al. Destructive twisting of neutral metalloproteases: the catalysis mechanism of the Dispase autolysis-inducing protein from Streptomyces mobaraensis DSM 40487. \u003cem\u003eFEBS J.\u003c/em\u003e \u003cb\u003e285\u003c/b\u003e, 4246\u0026ndash;4264. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/febs.14647\u003c/span\u003e\u003cspan address=\"10.1111/febs.14647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehdi, S. F. et al. Glucagon-like peptide-1: a multi-faceted anti-inflammatory agent. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1148209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1148209\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1148209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmezcua-Castillo, E. et al. C-Reactive Protein: The Quintessential Marker of Systemic Inflammation in Coronary Artery Disease-Advancing toward Precision Medicine. \u003cem\u003eBiomedicines\u003c/em\u003e 11, doi: (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biomedicines11092444\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines11092444\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSortino, O. et al. The Effects of Recombinant Human Lactoferrin on Immune Activation and the Intestinal Microbiome Among Persons Living with Human Immunodeficiency Virus and Receiving Antiretroviral Therapy. \u003cem\u003eJ. Infect. Dis.\u003c/em\u003e \u003cb\u003e219\u003c/b\u003e, 1963\u0026ndash;1968. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/infdis/jiz042\u003c/span\u003e\u003cspan address=\"10.1093/infdis/jiz042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgier, J., Efenberger, M. \u0026amp; Brzezińska-Błaszczyk, E. Cathelicidin impact on inflammatory cells. \u003cem\u003eCent. Eur. J. Immunol.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 225\u0026ndash;235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5114/ceji.2015.51359\u003c/span\u003e\u003cspan address=\"10.5114/ceji.2015.51359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDominguez, C., Boelens, R. \u0026amp; Bonvin, A. M. J. J. HADDOCK: A Protein\u0026thinsp;\u0026ndash;\u0026thinsp;Protein Docking Approach Based on Biochemical or Biophysical Information. \u003cem\u003eJ. Am. Chem. Soc.\u003c/em\u003e \u003cb\u003e125\u003c/b\u003e, 1731\u0026ndash;1737. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/ja026939x\u003c/span\u003e\u003cspan address=\"10.1021/ja026939x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, L. et al. Novel peptide inhibitors of angiotensin-converting enzyme 2. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e278\u003c/b\u003e, 15532\u0026ndash;15540. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M212934200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M212934200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJumper, J. et al. Highly accurate protein structure prediction with AlphaFold. \u003cem\u003eNature\u003c/em\u003e. \u003cb\u003e596\u003c/b\u003e, 583\u0026ndash;589. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-021-03819-2\u003c/span\u003e\u003cspan address=\"10.1038/s41586-021-03819-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVangone, A. \u0026amp; Bonvin, A. P. R. O. D. I. G. Y. A Contact-based Predictor of Binding Affinity in Protein-protein Complexes. \u003cem\u003eBIO-PROTOCOL\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21769/BioProtoc.2124\u003c/span\u003e\u003cspan address=\"10.21769/BioProtoc.2124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrassmann, G. et al. Computational Approaches to Predict Protein\u0026ndash;Protein Interactions in Crowded Cellular Environments. \u003cem\u003eChem. Rev.\u003c/em\u003e \u003cb\u003e124\u003c/b\u003e, 3932\u0026ndash;3977. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.chemrev.3c00550\u003c/span\u003e\u003cspan address=\"10.1021/acs.chemrev.3c00550\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePronk, S. et al. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. \u003cem\u003eBioinformatics\u003c/em\u003e. \u003cb\u003e29\u003c/b\u003e, 845\u0026ndash;854. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btt055\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btt055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobertson, M. J., Tirado-Rives, J. \u0026amp; Jorgensen, W. L. Improved Peptide and Protein Torsional Energetics with the OPLSAA Force Field. \u003cem\u003eJ. Chem. Theory Comput.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 3499\u0026ndash;3509. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.jctc.5b00356\u003c/span\u003e\u003cspan address=\"10.1021/acs.jctc.5b00356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlotaiq, N., Dermawan, D. \u0026amp; Elwali, N. E. Leveraging Therapeutic Proteins and Peptides from Lumbricus Earthworms: Targeting SOCS2 E3 Ligase for Cardiovascular Therapy through Molecular Dynamics Simulations. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 10818 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuet, P. \u0026amp; Blankschtein, D. Molecular Dynamics Simulation Study of Water Surfaces: Comparison of Flexible Water Models. \u003cem\u003eJ. Phys. Chem.\u003c/em\u003e \u003cb\u003e114\u003c/b\u003e, 13786\u0026ndash;13795. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/jp1067022\u003c/span\u003e\u003cspan address=\"10.1021/jp1067022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaini, R. S. et al. Dental biomaterials redefined: molecular docking and dynamics-driven dental resin composite optimization. \u003cem\u003eBMC Oral Health\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e, 557. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12903-024-04343-1\u003c/span\u003e\u003cspan address=\"10.1186/s12903-024-04343-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe PyMOL Molecular Graphics System v. 2.4. (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePettersen, E. F. et al. UCSF Chimera\u0026ndash;a visualization system for exploratory research and analysis. \u003cem\u003eJ. Comput. Chem.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 1605\u0026ndash;1612. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcc.20084\u003c/span\u003e\u003cspan address=\"10.1002/jcc.20084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian, S. et al. Assessing an ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility. \u003cem\u003eJ. Chem. Inf. Model.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e, 2664\u0026ndash;2679. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/ci500414b\u003c/span\u003e\u003cspan address=\"10.1021/ci500414b\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan, Z. et al. Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 1\u0026ndash;24 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRifai, E. A., Ferrario, V., Pleiss, J. \u0026amp; Geerke, D. P. Combined Linear Interaction Energy and Alchemical Solvation Free-Energy Approach for Protein-Binding Affinity Computation. \u003cem\u003eJ. Chem. Theory Comput.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 1300\u0026ndash;1310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.jctc.9b00890\u003c/span\u003e\u003cspan address=\"10.1021/acs.jctc.9b00890\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVald\u0026eacute;s-Tresanco, M. S., Vald\u0026eacute;s-Tresanco, M. E., Valiente, P. A., Moreno, E. \u0026amp; gmx_MMPBSA A New Tool to Perform End-State Free Energy Calculations with GROMACS. \u003cem\u003eJ. Chem. Theory Comput.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 6281\u0026ndash;6291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.jctc.1c00645\u003c/span\u003e\u003cspan address=\"10.1021/acs.jctc.1c00645\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller, B. R. 3 et al. MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. \u003cem\u003eJ. Chem. Theory Comput.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 3314\u0026ndash;3321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/ct300418h\u003c/span\u003e\u003cspan address=\"10.1021/ct300418h\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanday, S. K. \u0026amp; Alexov, E. Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation. \u003cem\u003eACS Omega\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, 11057\u0026ndash;11067. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acsomega.1c07037\u003c/span\u003e\u003cspan address=\"10.1021/acsomega.1c07037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimmons, P. B. \u0026amp; Hewage, C. M. HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 10869. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-020-67701-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-67701-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antimicrobial peptides, Cardiovascular disease, Molecular docking, Molecular dynamics","lastPublishedDoi":"10.21203/rs.3.rs-5376324/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5376324/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInfection-related cardiovascular diseases (CVDs) pose a significant health challenge, driving the need for novel therapeutic strategies to target key receptors involved in inflammation and infection. Antimicrobial peptides (AMPs) show the potential to disrupt pathogenic processes and offer a promising approach to CVD treatment. This study investigates the binding potential of selected AMPs with critical receptors implicated in CVDs, aiming to explore their therapeutic potential. A comprehensive computational approach was employed to assess AMP interactions with CVD-related receptors, including ACE2, CRP, MMP9, NLRP3, and TLR4. Molecular docking studies identified AMPs with high binding affinities to these targets, notably Tachystatin, Pleurocidin, and Subtilisin A, which showed strong interactions with ACE2, CRP, and MMP9. Following docking, 100 ns molecular dynamics (MD) simulations confirmed the stability of AMP-receptor complexes, and MM/PBSA calculations provided quantitative insights into binding energies, underscoring the potential of these AMPs to modulate receptor activity in infection and inflammation contexts. The study highlights the therapeutic potential of Tachystatin, Pleurocidin, and Subtilisin A in targeting infection-related pathways in CVDs. These AMPs demonstrate promising receptor binding properties and stability in computational models. Future research should focus on \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies to confirm their efficacy and safety, paving the way for potential clinical applications in managing infection-related cardiovascular conditions.\u003c/p\u003e","manuscriptTitle":"Computational analysis of antimicrobial peptides targeting key receptors in infection-related cardiovascular diseases: Molecular docking and dynamics insights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 14:50:28","doi":"10.21203/rs.3.rs-5376324/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-26T07:30:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-24T07:36:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-28T12:00:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169090508788236810060593631976680412645","date":"2024-11-18T06:27:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96661051694828078445533263153689787605","date":"2024-11-18T04:19:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-18T04:10:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-15T06:46:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-15T06:11:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-14T13:42:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-11-02T04:12:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0149ca2e-9c5a-4c9f-b840-5e09779ccba2","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41035582,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":41035583,"name":"Biological sciences/Drug discovery"},{"id":41035584,"name":"Health sciences/Molecular medicine"}],"tags":[],"updatedAt":"2025-03-17T16:03:05+00:00","versionOfRecord":{"articleIdentity":"rs-5376324","link":"https://doi.org/10.1038/s41598-025-93683-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-14 15:57:59","publishedOnDateReadable":"March 14th, 2025"},"versionCreatedAt":"2024-12-02 14:50:28","video":"","vorDoi":"10.1038/s41598-025-93683-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-93683-1","workflowStages":[]},"version":"v1","identity":"rs-5376324","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5376324","identity":"rs-5376324","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}