Dual Action Potential: Bromophenol in Combatting COVID-19 and Modulating ACE2-Mediated Circulatory Hemostasis: A Molecular Modeling Study

Dual Action Potential: Bromophenol in Combatting COVID-19 and Modulating ACE2-Mediated Circulatory Hemostasis: A Molecular Modeling Study | 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 Research Article Dual Action Potential: Bromophenol in Combatting COVID-19 and Modulating ACE2-Mediated Circulatory Hemostasis: A Molecular Modeling Study Kağan Tolga CİNİSLİ, Azizeh SHADIDIZAJI, Burak ÇINAR, Mohsen REZAİ, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4017904/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The possible recurrent threat of the COVID-19 pandemic driven by SARS-CoV-2 underscores the critical need for innovative pharmaceutical interventions targeting Angiotensin-Converting Enzyme 2 ( ACE2) receptors. Beyond the recognized role of ACE2 in viral entry, its intricate involvement in circulatory hemostasis, with potential hypotension-related complications, necessitates a comprehensive approach. This in silico study investigates the therapeutic potential of Bromophenol (BP) derived from Halophitys incurves (HIE) against both ACE2 -mediated viral entry and circulatory complications, particularly hypotension. Utilizing advanced in silico techniques; we assessed the pharmacokinetic parameters of BP through SWISSADME, ADME/T, and Swisstargetprediction. The Molecular Dynamics Simulation analysis further substantiated the favorable interactions within the BP- ACE2 complex. The results elucidated a favorable performance of BP in comparison to β-D-Mannose, serving as a potent inhibitor in impeding ACE2 -mediated viral entry and contributing to the regulation of circulatory hemostasis. This inquiry emphasizes BP's potential as a robust inhibitor against the multifaceted actions of ACE2 , offering valuable insights into its therapeutic effectiveness against COVID-19 . Additionally, it contributes to a deeper understanding of ACE2- mediated circulatory hemostasis by revealing BP's regulatory role in this physiological process. The encouraging findings warrant further exploration of BP as a novel therapeutic agent targeting ACE2 -induced dual unfavorable actions. Angiotensin-Converting Enzyme 2 (ACE2) Bromophenol Covid-19 Molecular Docking Molecular Dynamic Circulatory hemostasis. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The emergence of the COVID-19 virus in December 2019 prompted global attention due to its high transmissibility and pathogenicity, leading to the designation of the novel coronavirus as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing Coronavirus Disease 2019 ( COVID-19 ). With a 1% fatality rate, the virus posed a substantial threat, resulting in a pandemic declared by the World Health Organization on March 11, 2020 [1]. Coronaviruses utilize the Spike (S) glycoprotein subunit 1, particularly the Angiotensin-Converting Enzyme 2 ( ACE2 ) receptor, for host cell entry. ACE2 , highly expressed on vascular endothelium, acts as the primary receptor for SARS-CoV-2, with a binding affinity approximately four times higher than other variants. Despite a 69% vaccination rate in the United States by March 2023, concerns persist regarding potential SARS-CoV-2 variants, emphasizing the need for ongoing prophylactic and therapeutic strategies [2, 3]. ACE2 , functioning as a carboxypeptidase, plays a pivotal role in the renin-angiotensin system (RAS) by converting vasoconstrictive Angiotensin II (Ang II) into the vasodilatory Angiotensin-(1-7) [Ang-(1-7)]. This counter-regulatory ACE2 /Ang-(1-7)/Mas receptor axis provides balance within the RAS, influencing cardiovascular homeostasis and immune modulation [4, 5]. The delicate balance maintained by ACE2 activation, essential for cardiovascular homeostasis, raises concerns about the risk of hypotension with excessive activation. ACE2 inhibitors emerge as potential therapeutic options, aiming to modulate ACE2 activity for precise blood pressure regulation. The relationship between AT1R blockers for hypertensive patients and ACE2 receptor overexpression adds complexity to the hypertension- COVID-19 interaction. Despite ACE2 's recognized protective role in cardiovascular hemostasis, the potential risk of hypovolemic complications requires further study, especially concerning postoperative hypotension and differential ACE2 expression in various organs [5-9] Amidst the multidisciplinary regulatory role of ACE2 , the development of novel ACE2 -specific inhibitors becomes crucial. Natural compounds, particularly those from marine sources, are known for their bioactive potential. Algae extracts, rich in biodiversity, offer diverse bioactive molecules, including anti-infective properties, making them promising for alternative drug development [10-12]. Recognizing the importance of molecular descriptors in drug development [13], Bromophenol compounds derived from marine sources exhibit antioxidant and carbonic anhydrase inhibitory effects [14-17] To advance understanding, this study presents an in silico examination of the potential inhibitory effect of Bromophenol (BP) on the ACE2 receptor, comparing it with the commonly used β-D-mannose as a control drug. 2. Material and methods 2.1. Preparation of synthetic Compound for Molecular Modeling According to previous literature, this compound was selected [18]. 2D structure of BP drawn and their Simplified Molecular Input Line Entry System (SMILES) canonical forms were subsequently obtained via ChemDraw software (https://chemistrydocs.com/chemdraw-pro-8-0/ ) [19]. Following this step, 3D conformers of these compounds were generated in SDF (Simulation Description Format) using Chem3D (https://library.bath.ac.uk/chemistry-software/chem3d ) software, which is extensively employed in academic and research institutions to facilitate tasks such as chemical drawing, 3D modeling, data analysis, and laboratory management [20]. 2.2. Ligand and protein preparation for docking Table 1 shows the crystal structure of the human ACE2 (ID: 6VW1), Protein Data Bank (PDB) format was obtained from the PDB RCSB (https://www.rcsb.org) , The PDB stands as an extensively acknowledged and exhaustive resource, aimed at the preservation and dissemination of three-dimensional structures of biological macromolecules, which entail proteins, nucleic acids, and multifaceted assemblies. The PDB repository serves as a storehouse for crystallographic and other structural data procured through experimental methodology then preparation step for ligand and receptor was done in UCSF Chimera.36 software. The PDB stands as an extensively acknowledged and exhaustive resource, aimed at the preservation and dissemination of three-dimensional structures of biological macromolecules, which entail proteins, nucleic acids, and multifaceted assemblies. The PDB repository serves as a storehouse for crystallographic and other structural data procured through an experimental methodology [21]. This step includes adding hydrogen bond, deleting nonstandard residue, deleteing water molecules, and adding charge. Energy minimization can help to improve the conformation of a molecule and make it more suitable. Then minimizing energy was done with UCSF/Chimera software (Fig 1; Table 1). 2.3. Molecular docking protocol For this, ligand flexibility and protein rigid structure docking was done. The importance of molecular docking is largely predicated upon the interplay between the flexibility of the ligand and the rigidity of the protein [22]. Rigidity strengthening is a vital mechanism for protein-ligand binding. A key determinant of the ligand's ability to adopt various conformations and binding modes is its level of flexibility, whereas the protein's rigidity has an important role in detecting the affinity binding and ligand specificity. A set of ten poses were calculated, wherein β- D-mannose was employed as the control and subjected to a docking process within the active site of the ACE2 receptor. After the preparing ligand and protein steps, residues of the active site ( ARG 273, HIS345, HIS505 ) were selected [23]. Then the PDB files of the target proteins and BP were subjected to AutoDock Vina32 to predict the structure of the protein- ligand complexes and to evaluate the binding energy (Chimera # Vina software). Grid box size 20, 20, 20, grid box center 108.826, 0.683657, 163.848 and 10 poses were set (Fig 2). AutoDock Vina is a program that has been established as open-source software for molecular docking. The said program is primarily utilized in predicting the binding modes as well as the affinities of small molecules to a target protein. The aforementioned process involves the molecular connection between the small molecules and the target protein [24]. To predict the ideal mode of binding between the target protein (IDs:6VW1) and BP, the results of molecular docking are analyzed using PLIP (Protein-Ligand Interaction Profiler) and maestro Schrodinger software (Fig 3,4; Table 2). Maestro is a software application that has been designed by Schrödinger to offer a graphical user interface, which in turn provides users with the opportunity to access their broad range of molecular modeling and simulation software tools (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index ). 2.4. SWISSADME/ADMET Prediction SMILES canonical of the BP was submitted in www.swisstargetprediction.com server to predict targets. SMILES is a standard format used to represent chemical structures as a linear string of characters [24]. Then pharmacokinetics, toxicity, and druglike properties was predicted. In addition, strict adherence to pivotal criteria, including the quintessential Lipinski's rules, Veber's Rule, Egan's Rule, the measurement of polar surface area (TPSA), coupled with meticulous deliberations concerning rotatable bonds, ADME properties, and the intricacies of P450 metabolism (SOM), assumes an indispensable and central role (http://www.swissadme.ch; https://preadmet.webservice.bmdrc.org ) (Table 3) [25]. Table 1. Details of ACE2 Proteins Methods R value Resolution Residues Number Control Docking Kcal/mol ACE2 PDB ID: 6VW1 X-ray Diffraction 0.229 2.68 597 β-D-Mannose, -6,7 2.5. Molecular Dynamic simulations We utilized the GROMACS 2023 software (https://ftp.gromacs.org/gromacs/gromacs-2023-dev.tar.gz ) [26]. for our study. In the following step, topology and coordinate files were prepared. The force field employed was CHARMM27, incorporating the intermolecular (nonbonded) potential represented as a sum of Lennard–Jones (LJ) force and pairwise Coulomb interaction. The long-range electrostatic force was defined using the particle mesh Ewald (PME) method. The numerical intermixture was executed through the velocity Verlet [27, 28]. The system, under periodic boundary conditions, was situated in a cubic water box comprising extended simple point-charge (SPC) water molecules, positioned 1 nm away from each side of the wall. Electrostatic neutrality of the system was examined, and 3 sodium ions were added for neutralization, resulting in a system with 3 NA + ions and 16745 solvent atoms. The energy minimization process, consisting of 50,000 steps for 2 fs, was followed by equilibration at a constant temperature (NVT) of 300 K using the Berendsen thermostat. The cutoff radius was set at 1.2 nm. Subsequently, the system underwent equilibration at constant pressure (NVT) of 1 bar. This step aimed to achieve optimal arrangement of solvent molecules around the solute. Finally, the system underwent a 100 ns main simulation run at 300 K temperature and 1 bar pressure. For simulating the ACE2 –ligand complex, the topology file for the ligand was generated using the SWISSPARAM server (http://www.swissparam.ch/) for the CHARMM27 force field. The topology parameters and ligand coordinates were then integrated with those of ACE2 . The molecular dynamics simulation of the protein–ligand complex mirrored the protein simulation, lasting 100 ns. All subsequent analyses related to the simulation, such as the evaluation of intermolecular hydrogen bonds, RMSD, Rg, RMSF, and SASA method, were conducted using the GROMACS 2023 software. The MD simulation was executed on Ubuntu 22.04 Linux, running on an Intel Core 12 Quad 6800K 3.6 GHz, with a Nvidia GTX 1080ti GPU and 16 GB RAM. In addition, IMOD online server https://imods.iqfr.csic.es was used for analyzing B-factor, deformability, covariance map, eigenvalues, and elastic network and variance. Table 2 . Interaction results of BP and ACE2 Hydrogen Bond Index Residue AA Distance H-A Distance D-A Donor Angle Protein Donor Side Chain Donor Atoms Acceptor Atoms 1 273AA ARG 2,24 3,14 150.40 V V 2084(Ng+) 4878(O2) 2 273AA ARG 2,21 3,12 151,45 V V 2085(Ng+) 4878(O2) 3 518AA ARG 2,12 3,07 159,94 V V 4081(Ng+) 4883(O3) 4 518A ARG 3,40 4,08 127,96 V V 4082(Ng+) 4883(O3) Table 3. SWISSADME/ADMET Prediction Β-D-Mannose, (Control) Ligand (Synthetic compound) Canonical Smiles C(C1C(C(C(C(O1)O)O)O)O)O O=C1CCC(N1Cc1cc(O)c(c(c1Br)Br)O)C(=O)[CH2+] Physicochemical Properties Molecular Formula C6H12O6 C13H12Br2NO4 Molecular weight g/mol 180.16 g/mol 406.05 g/mol Num. Rotatable Bonds 1 3.00 Num. H-bond acceptors 6 4.00 Num. H-bond Donor 5 2.00 Molar Refractivity 35.74 84.29 TPSA 110.38 Ų 77.84 Ų Lipophilicity Consensus Log P o/w - 2,26 2.00 Water Solubility Log S (ESOL) 1,15 -3,48 Pharmacokinetics and Toxicity GI absorption Low High Ames test Mutagen Mutagen Caco2 2.56748 20.1055 HIA 22.355048 94.470994 Plasma Protein Binding 7.312029 88.587673 Mutagenic Mouse Negative Negative hERG inhibition Low risk Low risk BBB permeant No No P-gp substrate Yes No CYP1A2 inhibitor No Yes CYP2C19 inhibitor No No CYP2C9 inhibitor No No CYP2D6 inhibitor No No CYP3A4 inhibitor No No Drug likeness Lipiniski Yes Yes Ghose No Yes Veber yes Yes Egan yes Yes Muegge No Yes Bioavailability Score 0.55 0.55 Medicinal Chemistry Synthetic accessibility 4.08 2.52 3. Results and Discussion 3.1. Molecular Docking 3.1.1. Interaction Analyses of the ligands into ACE2 active site As shown in table 2,the BP/ ACE2 complex have 2 hydrogen bonds with ARG 273 residue and β-D- mannose/ ACE2 complex have one hydrogen bond with HIS 345 residue in active site. Hydrogen bonds represent a fundamental and pivotal form of interaction among biologically significant molecules. These bonds have an important role in shaping the higher-order structures of proteins, as well as mediating crucial protein-ligand interactions. In particular, hydrogen bonds assume a central role in the stabilization of protein-ligand complexes, thus playing a critical role in determining the selectivity of these interactions. This underscores the essential contribution of hydrogen bonds in molecular recognition processes, which has far-reaching implications in the context of biological function and drug design [29, 30] The BP presents the dock score of the hydrogen bond with –5,9 Kcal/mol (7 pose) the dock score of the hydrogen bond β-D- Mannose is -4,3 Kcal/mol (6 pose). Docking scores serve as valuable metrics for approximating the binding affinity between a ligand and its target, where lower scores correspond to stronger binding interactions. This critical aspect of docking analysis enables the assessment and comparison of potential ligand-protein affinities, guiding the selection and optimization of ligands with superior binding characteristics for further study or development [31]. However, in contrast, β-D-Mannose exhibits a reduced molecular weight in comparison to the BP. Despite this, it produces scores that approach the higher end (-4.3 kcal/mol), in contrast to the BP score (-5.9 kcal/mol). This suggests that the interaction between the 6VW1 and BP exhibits notable stability, indicating an enhanced binding affinity. These findings indicate that the BP qualifies as a potent inhibitor of the ACE2 enzyme, compared to the control compound under investigation (Table 2; Fig 3,4). 3.2. Result of SWISSADME/ADMET Prediction Lipinski's Rule of Five is a widely used concept in drug discovery to predict whether a biologically active molecule is likely to be suitable for oral administration [32]. The rule is based on specific properties, including molecular mass less than 500 Dalton, low lipophilicity (LogP) less than 5, fewer than 5 hydrogen bond donors, fewer than 10 hydrogen bond acceptors, and molar refractivity between 40-130 [33]. According to the rule, an orally active drug should not violate more than one of these conditions. This rule is crucial in drug development to guide the optimization of lead compound for both activity and selectivity while ensuring they maintain drug-like physicochemical properties as outlined by Lipinski's rule [34]. The results of this study are shown in the Table 3, molecular weights of BP and β-D- Mannose were 406.05 and 180.16 g/mol, respectively. Also values of the TPSA for BP and β-D- Mannose were 77.84 Å2 and 110,38 Å2, respectively (Table 3). The lowest TPSA values always give good results, so we note that the molecules from the HIE are better behaved than the β-D- Mannose. The results of this study indicated that BP derived from HIE have demonstrated superior properties compared to the β-D- Mannose. Other studies have also reported BP with better docking scores and significant molecular interactions with the active site residues of the SARS-CoV-2 virus. These findings suggest that BP may have potential therapeutic benefits and warrant further exploration for various applications [35]. Solubility plays a crucial role in drug discovery and development [36]. The LogS value is a measure of aqueous solubility, and compounds with higher LogS values tend to be more soluble. The BP showed a relatively low LogS solubility value of -3.71, which is important to consider. Low solubility can result in poor absorption and distribution, and ultimately affecting the effectiveness of a drug [37]. The BP has two hydrogen bonding donors and four hydrogen bonding acceptors, which are significant in drug design, as they contribute to interaction stability and specificity with the target. These hydrogen bonding properties also impact solubility and other physicochemical properties. Additionally, the BP displaied a molar refractivity value of 84.29, which measures polarizability, influencing molecule interactions and solubility. The typical therapeutic agent molar refractivity range is between 40 and 130 [38, 39]. Hence, it can be postulated that the BP, derived from marine organisms, effectively satisfies Lipinski's quintet of rules. Moreover, the compliance of the scrutinized BP with Veber's rule, which is indicative of the potential oral bioavailability of a possible drug candidate, is also observed. In addition, the adherence of the BP to Egan's rule, which is relevant to drug molecule absorption, as well as Ghose's and Muegge's rules, is discerned. The quantification of the ease of synthesizing a pharmaceutical compound is accomplished through the utilization of the SA. This quantitative measure evaluates the feasibility of synthesizing a drug molecule based on the prevalence of molecular fragments within publicly available databases. Therefore, the SA functions as an evaluative parameter to approximate the synthetic tractability of a given molecule. The SA score is derived by summing up contributions from all fragments in the molecule and then dividing this sum by the total number of fragments. This score is valuable in drug discovery as it helps evaluate the synthesis ease of a molecule, aiding various aspects of the drug development process [40-43].The molecule which gives a low score is easy to synthesize it, so SA for BP is 2,5. According to the ADME results, in terms of Absorption, the ligand exhibited significant gastrointestinal (GI) absorption potential. Regarding metabolism, the ligand interacts with microsomal cytochrome P450 enzymes, which are responsible for metabolizing many drugs. The results indicated that the BP has an inhibitory effect on CYP1A2, ensuring the ligand's prolonged action [44, 45]. As displayed in Table 3, in the context of Absorption, it is noteworthy that the BP exhibited an optimal Caco-2 permeability value (20.1055), unlike β-D-Mannose, which showed a relatively lower Caco-2 permeability value (2.56748). This diagnostic assessment plays a pivotal role in assessing the likelihood of effective intestinal absorption for the drug compound. In terms of absorption, the ligand demonstrated superior Caco-2 permeability compared to the control. Therefore, considering the extensive ADME evaluation, it is a reasonable conclusion that the ligand shows a notably higher potential for intestinal absorption when compared to β-D-Mannose. This observation implies that the BP may possess superior oral bioavailability with effective absorption following oral administration [46, 47]. The BP demonstrated a significant result in the Human Intestinal Absorption (HIA) test, suggesting a likelihood of easy absorption in the human intestinal environment [48, 49]. In this context, the BP exhibited a notable binding capability, as evidenced by a substantial value (88.587673) for plasma protein binding. This aspect bears critical importance in the development of novel drugs because the drug's biologically active form must be in an unbound state to cross biological barriers and produce a therapeutic effect [49, 50]. Moreover, antioxidant activities of BP are crucial in providing protection against oxidative stress and potential disease prevention [51]. The anticancer properties, including inhibition of cancer cell growth and apoptosis induction, make them promising for cancer treatment and prevention [52]. The documented antimicrobial activity against various microorganisms, including bacteria and fungi, is a valuable asset in the fight against antimicrobial resistance, and requires further investigation. Additionally, select bromophenol exhibited anti-diabetic properties, regulating blood glucose levels and improving insulin sensitivity, highlighting their potential in diabetes management. These findings collectively underscore the need for comprehensive research to fully understand the mechanisms and clinical applications of these compounds [53]. 3.3. Target prediction Swiss target prediction web server possesses the ability to forecast the most plausible protein targets of small molecules, as stated by Gfeller et al. (2014). Exploring molecular targets holds significant value in identifying potential phenotypical adverse reactions or cross-reactivity arising from the action of marine-derived materials, such as BP. The findings for the top 25 targets are depicted in Figure 5. The likely binding sites for the compound are primarily linked to protease targets, family A G protein-coupled receptors, and enzymes that modulate drug responses. 3.4. MD Simulation An MD simulation study was conducted to assess the stability of the ACE2 receptor when complexed with BP [54]. The results of MD by GROMACS software were shown in figure 6. The Rg values reveal that the protein is more compact before docking compared to after docking, with a measurement of approximately 2.02 nm (Fig6,B). The SASA results illustrate that the solvent-accessible surface area of the protein was greater before docking compared to the post-docking state (Fig6, C). Examining the RMSF of amino acids within the ligand's active site indicates that ligand binding to the protein in the active site does not induce fluctuations in residues, maintaining the stability of the structure (Fig6, D). In terms of RMSD, the analysis indicates that the complex exhibits an approximate value of 0.19 nm, demonstrating optimization compared to the protein alone (Fig6, E). The results of Imod server was shown in Figure 7.The MD simulations examined the BP/ ACE2 receptor complex, including Normal Mode Analysis (NMA), which calculates a molecule's vibrational modes and provides insights into its flexibility and dynamics. Deformability Assessment gauges a molecule's capacity for conformational changes, revealing its stability. Methods like B-Factor Visualization, Eigenvalue Determination, and Variance Representation analyze atomic mobility, collective motions, and flexibility, while Covariance Mapping explores correlated motions. Elastic Network Diagrams depict molecule stiffness and interactions, aiding in understanding structural stability and flexibility. Results are illustrated in Figure 7, including NMA in Figure 7a, the peaks in Figure 7b reveal the deformability of the respective molecule in Figure 7b. The empirical B-factor graphs, as shown in Figure 7c, were derived from the relevant PDB data and NMA mobility of the complexes. Figure 7d displays the computed eigenvalues of the docked complexes, which depict the rigidity of protein motion. The BP/ ACE2 complex stands out for requiring the least energy to undergo structural deformation, as indicated by the lowest eigenvalue (8.897083e-05) compared to the β-D-mannose/ ACE2 complex. Figure 7e demonstrates the inverse relationship between eigenvalue and associated variance. Individual variance is shown with red color, while variance is depicted with green color. The interaction patterns between residue pairs are show cased in the co-variance map (Figure 7f), where red indicates correlated motion, white signifies uncorrelated motion, and blue denotes anti-correlated motion between residue pairs. The elastic network model, depicted in the elastic map (Figure 7g), illustrates inter-atomic connections using dots. The color gradient of these dots corresponds directly to their stiffness, with darker spots representing stiffer connections. 4. CONCLUSION The findings of this investigation underscore the considerable potential of BP sourced from marine origins as an innovative therapeutic agent, offering valuable insights into the intricate role of ACE2 in circulatory hemostasis regulation and the prevention of Covid-19 viral entry. The comprehensive assessment of diverse parameters has revealed substantial enhancements compared to the control, affirming the robustness and feasibility of BP as a promising drug candidate. The elevated scores observed across a spectrum of critical parameters highlight the appropriateness of BP for further exploration and experimental validation. These discoveries unveil unexplored avenues for pharmaceutical research, particularly in the realm of circulatory modulation and the mitigation of the Covid-19 pandemic. While in-silico studies lay the foundation, subsequent in vitro and in vivo experiments are imperative to validate the efficacy and safety of BP as a viable treatment option. The progression of this finding from a theoretical concept to a tangible therapeutic solution will require multidisciplinary collaborative efforts among computational biologists, medicinal chemists, and virologists. This interdisciplinary approach is pivotal in advancing the understanding and application of BP, ultimately contributing to the development of innovative solutions for circulatory regulation and combating the challenges posed by the Covid-19 pandemic. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interest or personal relationship that could influence this work. References T. P. Sheahan and M. B. Frieman, "The continued epidemic threat of SARS-CoV-2 and implications for the future of global public health," Current Opinion in Virology, vol. 40, pp. 37-40, 2020. E. J. 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Eswaramoorthy, "Pharmacokinetics and drug-likeness of antidiabetic flavonoids: Molecular docking and DFT study," Plos one, vol. 16, no. 12, p. e0260853, 2021. P. V. Zadorozhnii, V. V. Kiselev, and A. V. Kharchenko, "In silico ADME profiling of salubrinal and its analogues," Future Pharmacology, vol. 2, no. 2, pp. 160-197, 2022. N. Zhao, F.-F. Guo, K.-Q. Xie, and T. Zeng, "Targeting Nrf-2 is a promising intervention approach for the prevention of ethanol-induced liver disease," Cellular and Molecular Life Sciences, vol. 75, pp. 3143-3157, 2018. A. Roohbakhsh, H. Parhiz, F. Soltani, R. Rezaee, and M. Iranshahi, "Molecular mechanisms behind the biological effects of hesperidin and hesperetin for the prevention of cancer and cardiovascular diseases," Life sciences, vol. 124, pp. 64-74, 2015. M. Liu, P. E. Hansen, and X. Lin, "Bromophenols in marine algae and their bioactivities," Marine Drugs, vol. 9, no. 7, pp. 1273-1292, 2011. N. Srivastava, P. Garg, P. Srivastava, and P. K. Seth, "A molecular dynamics simulation study of the ACE2 receptor with screened natural inhibitors to identify novel drug candidate against COVID-19," PeerJ, vol. 9, p. e11171, 2021. Additional Declarations Competing interest reported. A competing interests statement for submission of the paper entitled: Dual Action Potential: Bromophenol in Combatting COVID-19 and Modulating ACE2-Mediated Circulatory Hemostasis: A Molecular Modeling Study for the Journal of Molecular Modeling. We declare no competing interests associated with this research. The authors declare that they have no financial, personal, or professional conflicts of interest that could influence the outcome or interpretation of the work presented in this manuscript. This study was conducted with transparency, integrity, and adherence to ethical standards. The authors have no affiliations or financial involvement with any organization or entity that has a direct interest in the subject matter or materials discussed in the manuscript. We confirm that there are no relationships or activities that could be perceived as potentially influencing the objectivity or impartiality of the reported research. This competing interests statement is provided to ensure full disclosure and transparency in the evaluation and publication of our work in the Journal of Molecular Modeling. Co-authors: Kağan Tolga CİNİSLİ, Azizeh SHADIDIZAJI*, Burak ÇINAR, Mohsen REZAİ, Sahar MEMARKASHANI, Ahmet HACIMÜFTUOĞLU, Mohamad WARDA* *Corresponding authors: Azizeh SHADIDIZAJI [email protected] Mohamad Warda [email protected] Cite Share Download PDF Status: Posted Version 1 posted 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-4017904","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277737972,"identity":"b294f193-c70f-4fa4-941d-7f55dba98ad8","order_by":0,"name":"Kağan Tolga CİNİSLİ","email":"","orcid":"","institution":"Ataturk University","correspondingAuthor":false,"prefix":"","firstName":"Kağan","middleName":"Tolga","lastName":"CİNİSLİ","suffix":""},{"id":277737973,"identity":"ef386960-1143-4dd9-90b5-b4e4d7405035","order_by":1,"name":"Azizeh SHADIDIZAJI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACPiBmBiIefgYGNuK0sMG0SDaQqoXB4ADRWvgPMH8uqLGWMb6R/OzBhwoGeX6xAwS0SCSwSc84ls5jdiPN3HDGGQbDmbMTCGlhYGPmYTsM1JJgJs3bxpBgcJuQFpDDeP4d5jGekf6NSC0MCQxAlYd5DCRyiLUF5BfevnQeiTNvyiRnnJEg7Bd+sMO+Wdvzt6dvk/hQYSPPL01AC1DTBwgtAFYpQUg5itYDpKgeBaNgFIyCkQQALyg1zS5SMgwAAAAASUVORK5CYII=","orcid":"","institution":"Ataturk University","correspondingAuthor":true,"prefix":"","firstName":"Azizeh","middleName":"","lastName":"SHADIDIZAJI","suffix":""},{"id":277737974,"identity":"cc4fd8a0-55df-48b7-9f4c-d5836704504f","order_by":2,"name":"Burak ÇINAR","email":"","orcid":"","institution":"Ataturk University","correspondingAuthor":false,"prefix":"","firstName":"Burak","middleName":"","lastName":"ÇINAR","suffix":""},{"id":277737975,"identity":"d223f5b5-522e-40b6-99a5-381bcd1ead7b","order_by":3,"name":"Mohsen REZAİ","email":"","orcid":"","institution":"Ataturk University","correspondingAuthor":false,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"REZAİ","suffix":""},{"id":277737976,"identity":"6ad2ffcf-e5cb-4f27-b78a-792916a00b5a","order_by":4,"name":"Sahar MEMARKASHANI","email":"","orcid":"","institution":"Guilan University","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"","lastName":"MEMARKASHANI","suffix":""},{"id":277737977,"identity":"1a32547f-0838-4ec9-a06a-0c2cdc296f7c","order_by":5,"name":"Ahmet HACIMÜFTUOĞLU","email":"","orcid":"","institution":"Ataturk University","correspondingAuthor":false,"prefix":"","firstName":"Ahmet","middleName":"","lastName":"HACIMÜFTUOĞLU","suffix":""},{"id":277737978,"identity":"594403ad-9a5a-4fdd-a1bd-f0e7ea9808f4","order_by":6,"name":"Mohamad WARDA","email":"","orcid":"","institution":"Ataturk University","correspondingAuthor":false,"prefix":"","firstName":"Mohamad","middleName":"","lastName":"WARDA","suffix":""}],"badges":[],"createdAt":"2024-03-05 16:10:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4017904/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4017904/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52403426,"identity":"b91cb467-c374-456a-9843-ded95f7d9a7e","added_by":"auto","created_at":"2024-03-11 08:12:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":289242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCovid-19,\u003c/em\u003e 3D Structure (PDB ID: 6VW1) structure images of the human \u003cem\u003eACE2 \u003c/em\u003e\u0026nbsp;enzyme of the \u003cem\u003eCovid-19\u003c/em\u003e receptor was acquired using Maestro Schrodinger software\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4017904/v1/bde21be26c21365f6fef871e.png"},{"id":52403427,"identity":"c7a5098b-d144-476b-8fab-b839ba28ade7","added_by":"auto","created_at":"2024-03-11 08:12:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":309890,"visible":true,"origin":"","legend":"\u003cp\u003e3D Structure of active site \u003cem\u003eACE2 -\u003c/em\u003e chain A (\u003cem\u003eHIS345, ARG 273, HIS 505\u003c/em\u003e). The structure was taken from Maestro Shrodinger Software\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4017904/v1/964ef9e3117476e6b6d43528.png"},{"id":52403428,"identity":"0623c7e8-6f19-4e80-98f1-cc281817f867","added_by":"auto","created_at":"2024-03-11 08:12:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":276480,"visible":true,"origin":"","legend":"\u003cp\u003eOptimal pose interactions involving the ligand and receptor are depicted in 3D. Specifically, the interactions between β-D mannose/\u003cem\u003eACE2\u003c/em\u003e (left) and BPs/\u003cem\u003eACE2\u003c/em\u003e (right) are presented. These visualizations were generated using Maestro Schrödinger Software.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4017904/v1/c92f2244609468ff3b63ee18.png"},{"id":52404603,"identity":"32343ef3-3cf9-4883-9de3-17d19b869aae","added_by":"auto","created_at":"2024-03-11 08:20:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165994,"visible":true,"origin":"","legend":"\u003cp\u003e2D representations of optimal pose interactions between β-D-Mannose/\u003cem\u003eACE2\u003c/em\u003e (left) and BP/\u003cem\u003eACE2\u003c/em\u003e (right) are illustrated. These visualizations were generated using Maestro Schrödinger Software.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4017904/v1/2cb21a59563b2450f0058abc.png"},{"id":52403424,"identity":"29a02c82-6716-4320-8be7-a5833e20f17b","added_by":"auto","created_at":"2024-03-11 08:12:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":146346,"visible":true,"origin":"","legend":"\u003cp\u003eGraphic showing the target structure prediction of the synthesized substance (\u003ca href=\"http://www.swisstargetprediction.ch/\"\u003ehttp://www.swisstargetprediction.ch\u003c/a\u003e ).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4017904/v1/da2ceeaeb5692da5e8ed1bab.png"},{"id":52404605,"identity":"a5a72703-4aed-4e1d-a3c4-5a7f87d02a06","added_by":"auto","created_at":"2024-03-11 08:20:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":180915,"visible":true,"origin":"","legend":"\u003cp\u003eResults MD by GROMACS software, Number of H bonds (A); Rg(nm)(B); RMSF(C);SASA (D); RMSD(E)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4017904/v1/7bb66b30b6438ac32394e75a.png"},{"id":52403430,"identity":"56057d84-40fb-48ed-91e1-0fe0f675ecd5","added_by":"auto","created_at":"2024-03-11 08:12:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":274681,"visible":true,"origin":"","legend":"\u003cp\u003eMD results for (BPs/\u003cem\u003e ACE2\u003c/em\u003e complex); (a) NMA, (b) deformability, (c) B-factor, (d) eigenvalues, (e) variance (f) co-variance map and (g) elastic network\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4017904/v1/0692f7b749349b4291cee5ce.png"},{"id":53348180,"identity":"91b69794-4f0e-4884-ba7b-1a8226dcba6d","added_by":"auto","created_at":"2024-03-24 22:22:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1975939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4017904/v1/6a8033df-330f-4432-8fed-c08691dff512.pdf"}],"financialInterests":"Competing interest reported. A competing interests statement for submission of the paper entitled: Dual Action Potential: Bromophenol in Combatting COVID-19 and Modulating ACE2-Mediated Circulatory Hemostasis: A Molecular Modeling Study for the Journal of Molecular Modeling.\nWe declare no competing interests associated with this research. The authors declare that they have no financial, personal, or professional conflicts of interest that could influence the outcome or interpretation of the work presented in this manuscript. This study was conducted with transparency, integrity, and adherence to ethical standards. The authors have no affiliations or financial involvement with any organization or entity that has a direct interest in the subject matter or materials discussed in the manuscript. We confirm that there are no relationships or activities that could be perceived as potentially influencing the objectivity or impartiality of the reported research. This competing interests statement is provided to ensure full disclosure and transparency in the evaluation and publication of our work in the Journal of Molecular Modeling.\nCo-authors: \nKağan Tolga CİNİSLİ, Azizeh SHADIDIZAJI*, Burak ÇINAR, Mohsen REZAİ, Sahar MEMARKASHANI, Ahmet HACIMÜFTUOĞLU, Mohamad WARDA*\n\n*Corresponding authors: \tAzizeh SHADIDIZAJI\[email protected] \nMohamad Warda\[email protected]","formattedTitle":"Dual Action Potential: Bromophenol in Combatting COVID-19 and Modulating ACE2-Mediated Circulatory Hemostasis: A Molecular Modeling Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe emergence of the \u003cem\u003eCOVID-19\u003c/em\u003e virus in December 2019 prompted global attention due to its high transmissibility and pathogenicity, leading to the designation of the novel coronavirus as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing Coronavirus Disease 2019 (\u003cem\u003eCOVID-19\u003c/em\u003e). With a 1% fatality rate, the virus posed a substantial threat, resulting in a pandemic declared by the World Health Organization on March 11, 2020 [1].\u003c/p\u003e\n\u003cp\u003eCoronaviruses utilize the Spike (S) glycoprotein subunit 1, particularly the Angiotensin-Converting Enzyme 2 (\u003cem\u003eACE2\u003c/em\u003e) receptor, for host cell entry. \u003cem\u003eACE2\u003c/em\u003e, highly expressed on vascular endothelium, acts as the primary receptor for SARS-CoV-2, with a binding affinity approximately four times higher than other variants. Despite a 69% vaccination rate in the United States by March 2023, concerns persist regarding potential SARS-CoV-2 variants, emphasizing the need for ongoing prophylactic and therapeutic strategies [2, 3].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eACE2\u003c/em\u003e, functioning as a carboxypeptidase, plays a pivotal role in the renin-angiotensin system (RAS) by converting vasoconstrictive Angiotensin II (Ang II) into the vasodilatory Angiotensin-(1-7) [Ang-(1-7)]. This counter-regulatory \u003cem\u003eACE2\u003c/em\u003e/Ang-(1-7)/Mas receptor axis provides balance within the RAS, influencing cardiovascular homeostasis and immune modulation [4, 5].\u003c/p\u003e\n\u003cp\u003eThe delicate balance maintained by \u003cem\u003eACE2 \u003c/em\u003eactivation, essential for cardiovascular homeostasis, raises concerns about the risk of hypotension with excessive activation. \u003cem\u003eACE2 \u003c/em\u003einhibitors emerge as potential therapeutic options, aiming to modulate \u003cem\u003eACE2 \u003c/em\u003eactivity for precise blood pressure regulation. The relationship between AT1R blockers for hypertensive patients and \u003cem\u003eACE2 \u003c/em\u003ereceptor overexpression adds complexity to the hypertension-\u003cem\u003eCOVID-19\u003c/em\u003e interaction. Despite \u003cem\u003eACE2\u003c/em\u003e\u0026apos;s recognized protective role in cardiovascular hemostasis, the potential risk of hypovolemic complications requires further study, especially concerning postoperative hypotension and differential \u003cem\u003eACE2 \u003c/em\u003eexpression in various organs [5-9]\u003c/p\u003e\n\u003cp\u003eAmidst the multidisciplinary regulatory role of \u003cem\u003eACE2\u003c/em\u003e, the development of novel \u003cem\u003eACE2\u003c/em\u003e -specific inhibitors becomes crucial. Natural compounds, particularly those from marine sources, are known for their bioactive potential. Algae extracts, rich in biodiversity, offer diverse bioactive molecules, including anti-infective properties, making them promising for alternative drug development [10-12]. Recognizing the importance of molecular descriptors in drug development [13], Bromophenol compounds derived from marine sources exhibit antioxidant and carbonic anhydrase inhibitory effects [14-17]\u003c/p\u003e\n\u003cp\u003eTo advance understanding, this study presents an \u003cem\u003ein silico\u003c/em\u003e examination of the potential inhibitory effect of Bromophenol (BP) on the \u003cem\u003eACE2 \u003c/em\u003ereceptor, comparing it with the commonly used \u0026beta;-D-mannose as a control drug.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Preparation of synthetic Compound for Molecular Modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to previous literature, this compound was selected [18]. 2D structure of BP drawn and their Simplified Molecular Input Line Entry System (SMILES) canonical forms were subsequently obtained via ChemDraw software (https://chemistrydocs.com/chemdraw-pro-8-0/ ) [19]. Following this step, 3D conformers of these compounds were generated in SDF (Simulation Description Format) using Chem3D (https://library.bath.ac.uk/chemistry-software/chem3d ) software, which is extensively employed in academic and research institutions to facilitate tasks such as chemical drawing, 3D modeling, data analysis, and laboratory management [20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Ligand and protein preparation for docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows the crystal structure of the human \u003cem\u003eACE2\u0026nbsp;\u003c/em\u003e(ID: 6VW1), Protein Data Bank (PDB) format was obtained from the PDB RCSB (https://www.rcsb.org) , The PDB stands as an extensively acknowledged and exhaustive resource, aimed at the preservation and dissemination of three-dimensional structures of biological macromolecules, which entail proteins, nucleic acids, and multifaceted assemblies. The PDB repository serves as a storehouse for crystallographic and other structural data procured through experimental methodology then preparation step for ligand and receptor was done in UCSF Chimera.36 software. The PDB stands as an extensively acknowledged and exhaustive resource, aimed at the preservation and dissemination of three-dimensional structures of biological macromolecules, which entail proteins, nucleic acids, and multifaceted assemblies. The PDB repository serves as a storehouse for crystallographic and other structural data procured through an experimental methodology [21]. This step includes adding hydrogen bond, deleting nonstandard residue, deleteing water molecules, and adding charge. Energy minimization can help to improve the conformation of a molecule and make it more suitable. Then minimizing energy was done with UCSF/Chimera software (Fig 1; Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Molecular docking protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this, ligand flexibility and protein rigid structure docking was done. The importance of molecular docking is largely predicated upon the interplay between the flexibility of the ligand and the rigidity of the protein [22]. Rigidity strengthening is a vital mechanism for protein-ligand binding. A key determinant of the ligand\u0026apos;s ability to adopt various conformations and binding modes is its level of flexibility, whereas the protein\u0026apos;s rigidity has an important role in detecting the affinity binding and ligand specificity. A set of ten poses were calculated, wherein \u0026beta;- D-mannose was employed as the control and subjected to a docking process within the active site of the \u003cem\u003eACE2\u0026nbsp;\u003c/em\u003e receptor. After the preparing ligand and protein steps, residues of the active site (\u003cem\u003eARG 273, HIS345, HIS505\u003c/em\u003e) were selected [23]. Then the PDB files of the target proteins and BP were subjected to AutoDock Vina32 to predict the structure of the protein- ligand complexes and to evaluate the binding energy (Chimera # Vina software). Grid box size 20, 20, 20, grid box center 108.826, 0.683657, 163.848 and 10 poses were set (Fig 2). AutoDock Vina is a program that has been established as open-source software for molecular docking. The said program is primarily utilized in predicting the binding modes as well as the affinities of small molecules to a target protein. The aforementioned process involves the molecular connection between the small molecules and the target protein [24]. To predict the ideal mode of binding between the target protein (IDs:6VW1) and BP, the results of molecular docking are analyzed using PLIP (Protein-Ligand Interaction Profiler) and maestro Schrodinger software (Fig 3,4; Table 2). Maestro is a software application that has been designed by Schr\u0026ouml;dinger to offer a graphical user interface, which in turn provides users with the opportunity to access their broad range of molecular modeling and simulation software tools (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. SWISSADME/ADMET Prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSMILES canonical of the BP was submitted in www.swisstargetprediction.com server to predict targets. SMILES is a standard format used to represent chemical structures as a linear string of characters [24]. Then pharmacokinetics, toxicity, and druglike properties was predicted. In addition, strict adherence to pivotal criteria, including the quintessential Lipinski\u0026apos;s rules, Veber\u0026apos;s Rule, Egan\u0026apos;s Rule, the measurement of polar surface area (TPSA), coupled with meticulous deliberations concerning rotatable bonds, ADME properties, and the intricacies of P450 metabolism (SOM), assumes an indispensable and central role (http://www.swissadme.ch; https://preadmet.webservice.bmdrc.org \u003cstrong\u003e)\u0026nbsp;\u003c/strong\u003e(Table 3) [25].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;1.\u0026nbsp;\u003c/strong\u003eDetails of \u003cem\u003eACE2\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.636963696369637%\" valign=\"bottom\"\u003e\n \u003cp\u003eProteins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.511551155115512%\" valign=\"bottom\"\u003e\n \u003cp\u003eMethods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.561056105610561%\" valign=\"bottom\"\u003e\n \u003cp\u003eR value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"bottom\"\u003e\n \u003cp\u003eResolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"bottom\"\u003e\n \u003cp\u003eResidues Number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.221122112211221%\" valign=\"bottom\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.336633663366335%\" valign=\"bottom\"\u003e\n \u003cp\u003eDocking Kcal/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.636963696369637%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eACE2\u003c/em\u003e PDB ID: 6VW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.511551155115512%\" valign=\"bottom\"\u003e\n \u003cp\u003eX-ray Diffraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.561056105610561%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.851485148514852%\" valign=\"bottom\"\u003e\n \u003cp\u003e597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.221122112211221%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026beta;-D-Mannose,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.336633663366335%\" valign=\"bottom\"\u003e\n \u003cp\u003e-6,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Molecular Dynamic simulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the GROMACS 2023 software (https://ftp.gromacs.org/gromacs/gromacs-2023-dev.tar.gz ) [26]. for our study. In the following step, topology and coordinate files were prepared. The force field employed was CHARMM27, incorporating the intermolecular (nonbonded) potential represented as a sum of Lennard\u0026ndash;Jones (LJ) force and pairwise Coulomb interaction. The long-range electrostatic force was defined using the particle mesh Ewald (PME) method. The numerical intermixture was executed through the velocity Verlet [27, 28]. The system, under periodic boundary conditions, was situated in a cubic water box comprising extended simple point-charge (SPC) water molecules, positioned 1 nm away from each side of the wall. Electrostatic neutrality of the system was examined, and 3 sodium ions were added for neutralization, resulting in a system with 3 NA\u003csup\u003e+\u003c/sup\u003e ions and 16745 solvent atoms. The energy minimization process, consisting of 50,000 steps for 2 fs, was followed by equilibration at a constant temperature (NVT) of 300 K using the Berendsen thermostat. The cutoff radius was set at 1.2 nm. Subsequently, the system underwent equilibration at constant pressure (NVT) of 1 bar. This step aimed to achieve optimal arrangement of solvent molecules around the solute. Finally, the system underwent a 100 ns main simulation run at 300 K temperature and 1 bar pressure.\u003c/p\u003e\n\u003cp\u003eFor simulating the \u003cem\u003eACE2\u003c/em\u003e \u0026ndash;ligand complex, the topology file for the ligand was generated using the SWISSPARAM server (http://www.swissparam.ch/) for the CHARMM27 force field. The topology parameters and ligand coordinates were then integrated with those of \u003cem\u003eACE2\u003c/em\u003e. The molecular dynamics simulation of the protein\u0026ndash;ligand complex mirrored the protein simulation, lasting 100 ns. All subsequent analyses related to the simulation, such as the evaluation of intermolecular hydrogen bonds, RMSD, Rg, RMSF, and SASA method, were conducted using the GROMACS 2023 software. The MD simulation was executed on Ubuntu 22.04 Linux, running on an Intel Core 12 Quad 6800K 3.6 GHz, with a Nvidia GTX 1080ti GPU and 16 GB RAM.\u003c/p\u003e\n\u003cp\u003eIn addition, IMOD online server https://imods.iqfr.csic.es was used for analyzing B-factor, deformability, covariance map, eigenvalues, and elastic network and variance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Interaction results of BP and \u003cem\u003eACE2\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"10\" valign=\"bottom\"\u003e\n \u003cp\u003eHydrogen Bond\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.2631578947368425%\" valign=\"bottom\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eResidue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"bottom\"\u003e\n \u003cp\u003eDistance H-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"bottom\"\u003e\n \u003cp\u003eDistance D-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003eDonor Angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"bottom\"\u003e\n \u003cp\u003eProtein Donor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003eSide Chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eDonor Atoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eAcceptor Atoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.2631578947368425%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003e273AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e150.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"bottom\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e2084(Ng+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003e4878(O2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.2631578947368425%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003e273AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e151,45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"bottom\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e2085(Ng+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003e4878(O2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.2631578947368425%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003e518AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e159,94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"bottom\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e4081(Ng+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003e4883(O3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.2631578947368425%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003e518A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e127,96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"bottom\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e4082(Ng+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003e4883(O3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eSWISSADME/ADMET Prediction\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.894039735099337%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.907284768211921%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.32450331125828%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Beta;-D-Mannose, (Control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.87417218543046%\" valign=\"top\"\u003e\n \u003cp\u003eLigand (Synthetic compound)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.894039735099337%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.907284768211921%\" valign=\"top\"\u003e\n \u003cp\u003eCanonical Smiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.32450331125828%\" valign=\"top\"\u003e\n \u003cp\u003eC(C1C(C(C(C(O1)O)O)O)O)O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.87417218543046%\" valign=\"top\"\u003e\n \u003cp\u003eO=C1CCC(N1Cc1cc(O)c(c(c1Br)Br)O)C(=O)[CH2+]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.894039735099337%\" rowspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysicochemical Properties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.907284768211921%\" valign=\"top\"\u003e\n \u003cp\u003eMolecular Formula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.32450331125828%\" valign=\"top\"\u003e\n \u003cp\u003eC6H12O6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.87417218543046%\" valign=\"top\"\u003e\n \u003cp\u003eC13H12Br2NO4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eMolecular weight g/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003e180.16 g/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003e406.05 g/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eNum. Rotatable Bonds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eNum. H-bond acceptors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eNum. H-bond Donor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eMolar Refractivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003e35.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003e84.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eTPSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003e110.38 \u0026Aring;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003e77.84 \u0026Aring;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.894039735099337%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLipophilicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.907284768211921%\" valign=\"top\"\u003e\n \u003cp\u003eConsensus Log P o/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.32450331125828%\" valign=\"top\"\u003e\n \u003cp\u003e- 2,26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.87417218543046%\" valign=\"top\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.894039735099337%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater Solubility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.907284768211921%\" valign=\"top\"\u003e\n \u003cp\u003eLog S (ESOL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.32450331125828%\" valign=\"top\"\u003e\n \u003cp\u003e1,15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.87417218543046%\" valign=\"top\"\u003e\n \u003cp\u003e-3,48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.894039735099337%\" rowspan=\"14\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePharmacokinetics and Toxicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.907284768211921%\" valign=\"top\"\u003e\n \u003cp\u003eGI absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.32450331125828%\" valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.87417218543046%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eAmes test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eCaco2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003e2.56748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003e20.1055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eHIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003e22.355048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003e94.470994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003ePlasma Protein Binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003e7.312029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003e88.587673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eMutagenic Mouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003ehERG inhibition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eBBB permeant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eP-gp substrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eCYP1A2 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eCYP2C19 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eCYP2C9 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eCYP2D6 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eCYP3A4 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.894039735099337%\" rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDrug likeness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.907284768211921%\" valign=\"top\"\u003e\n \u003cp\u003eLipiniski\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.32450331125828%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.87417218543046%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eGhose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eVeber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eEgan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eMuegge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.53543307086614%\" valign=\"top\"\u003e\n \u003cp\u003eBioavailability Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.299212598425196%\" valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.16535433070866%\" valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.894039735099337%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedicinal Chemistry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.907284768211921%\" valign=\"top\"\u003e\n \u003cp\u003eSynthetic accessibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.32450331125828%\" valign=\"top\"\u003e\n \u003cp\u003e4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.87417218543046%\" valign=\"top\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1. Molecular Docking \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.1. Interaction Analyses of the ligands into \u003cem\u003eACE2 \u003c/em\u003e active site\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in table 2,the BP/\u003cem\u003eACE2 \u003c/em\u003e complex have 2 hydrogen bonds with \u003cem\u003eARG\u003c/em\u003e273 residue and \u0026beta;-D- mannose/\u003cem\u003eACE2 \u003c/em\u003e complex have one hydrogen bond with \u003cem\u003eHIS\u003c/em\u003e 345 residue in active site. Hydrogen bonds represent a fundamental and pivotal form of interaction among biologically significant molecules. These bonds have an important role in shaping the higher-order structures of proteins, as well as mediating crucial protein-ligand interactions. In particular, hydrogen bonds assume a central role in the stabilization of protein-ligand complexes, thus playing a critical role in determining the selectivity of these interactions. This underscores the essential contribution of hydrogen bonds in molecular recognition processes, which has far-reaching implications in the context of biological function and drug design [29, 30] The BP presents the dock score of the hydrogen bond with \u0026ndash;5,9 Kcal/mol (7 pose) the dock score of the hydrogen bond \u0026beta;-D- Mannose is -4,3 Kcal/mol (6 pose). Docking scores serve as valuable metrics for approximating the binding affinity between a ligand and its target, where lower scores correspond to stronger binding interactions. This critical aspect of docking analysis enables the assessment and comparison of potential ligand-protein affinities, guiding the selection and optimization of ligands with superior binding characteristics for further study or development [31]. However, in contrast, \u0026beta;-D-Mannose exhibits a reduced molecular weight in comparison to the BP. Despite this, it produces scores that approach the higher end (-4.3 kcal/mol), in contrast to the BP score (-5.9 kcal/mol). This suggests that the interaction between the 6VW1 and BP exhibits notable stability, indicating an enhanced binding affinity. These findings indicate that the BP qualifies as a potent inhibitor of the \u003cem\u003eACE2\u003c/em\u003e enzyme, compared to the control compound under investigation (Table 2; Fig 3,4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Result of SWISSADME/ADMET Prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLipinski\u0026apos;s Rule of Five is a widely used concept in drug discovery to predict whether a biologically active molecule is likely to be suitable for oral administration [32]. The rule is based on specific properties, including molecular mass less than 500 Dalton, low lipophilicity (LogP) less than 5, fewer than 5 hydrogen bond donors, fewer than 10 hydrogen bond acceptors, and molar refractivity between 40-130 [33]. According to the rule, an orally active drug should not violate more than one of these conditions. This rule is crucial in drug development to guide the optimization of lead compound for both activity and selectivity while ensuring they maintain drug-like physicochemical properties as outlined by Lipinski\u0026apos;s rule [34]. The results of this study are shown in the Table 3, molecular weights of BP and \u0026beta;-D- Mannose were 406.05 and 180.16 g/mol, respectively. Also values of the TPSA for BP and \u0026beta;-D- Mannose were 77.84 \u0026Aring;2 and 110,38 \u0026Aring;2, respectively (Table 3). The lowest TPSA values always give good results, so we note that the molecules from the \u003cem\u003eHIE\u003c/em\u003e are better behaved than the \u0026beta;-D- Mannose. The results of this study indicated that BP derived from \u003cem\u003eHIE \u003c/em\u003ehave demonstrated superior properties compared to the \u0026beta;-D- Mannose. Other studies have also reported BP with better docking scores and significant molecular interactions with the active site residues of the \u003cem\u003eSARS-CoV-2\u003c/em\u003e virus. These findings suggest that BP may have potential therapeutic benefits and warrant further exploration for various applications [35]. Solubility plays a crucial role in drug discovery and development [36]. The LogS value is a measure of aqueous solubility, and compounds with higher LogS values tend to be more soluble. The BP showed a relatively low LogS solubility value of -3.71, which is important to consider. Low solubility can result in poor absorption and distribution, and ultimately affecting the effectiveness of a drug [37]. The BP has two hydrogen bonding donors and four hydrogen bonding acceptors, which are significant in drug design, as they contribute to interaction stability and specificity with the target. These hydrogen bonding properties also impact solubility and other physicochemical properties. Additionally, the BP displaied a molar refractivity value of 84.29, which measures polarizability, influencing molecule interactions and solubility. The typical therapeutic agent molar refractivity range is between 40 and 130 [38, 39]. Hence, it can be postulated that the BP, derived from marine organisms, effectively satisfies Lipinski\u0026apos;s quintet of rules. Moreover, the compliance of the scrutinized BP with Veber\u0026apos;s rule, which is indicative of the potential oral bioavailability of a possible drug candidate, is also observed. In addition, the adherence of the BP to Egan\u0026apos;s rule, which is relevant to drug molecule absorption, as well as Ghose\u0026apos;s and Muegge\u0026apos;s rules, is discerned.\u003c/p\u003e\n\u003cp\u003eThe quantification of the ease of synthesizing a pharmaceutical compound is accomplished through the utilization of the SA. This quantitative measure evaluates the feasibility of synthesizing a drug molecule based on the prevalence of molecular fragments within publicly available databases. Therefore, the SA functions as an evaluative parameter to approximate the synthetic tractability of a given molecule. The SA score is derived by summing up contributions from all fragments in the molecule and then dividing this sum by the total number of fragments. This score is valuable in drug discovery as it helps evaluate the synthesis ease of a molecule, aiding various aspects of the drug development process [40-43].The molecule which gives a low score is easy to synthesize it, so SA for BP is 2,5. \u003c/p\u003e\n\u003cp\u003eAccording to the ADME results, in terms of Absorption, the ligand exhibited significant gastrointestinal (GI) absorption potential. Regarding metabolism, the ligand interacts with microsomal cytochrome P450 enzymes, which are responsible for metabolizing many drugs. The results indicated that the BP has an inhibitory effect on CYP1A2, ensuring the ligand\u0026apos;s prolonged action [44, 45]. As displayed in Table 3, in the context of Absorption, it is noteworthy that the BP exhibited an optimal Caco-2 permeability value (20.1055), unlike \u0026beta;-D-Mannose, which showed a relatively lower Caco-2 permeability value (2.56748). This diagnostic assessment plays a pivotal role in assessing the likelihood of effective intestinal absorption for the drug compound. In terms of absorption, the ligand demonstrated superior Caco-2 permeability compared to the control. Therefore, considering the extensive ADME evaluation, it is a reasonable conclusion that the ligand shows a notably higher potential for intestinal absorption when compared to \u0026beta;-D-Mannose. This observation implies that the BP may possess superior oral bioavailability with effective absorption following oral administration [46, 47]. The BP demonstrated a significant result in the Human Intestinal Absorption (HIA) test, suggesting a likelihood of easy absorption in the human intestinal environment [48, 49]. In this context, the BP exhibited a notable binding capability, as evidenced by a substantial value (88.587673) for plasma protein binding. This aspect bears critical importance in the development of novel drugs because the drug\u0026apos;s biologically active form must be in an unbound state to cross biological barriers and produce a therapeutic effect [49, 50]. Moreover, antioxidant activities of BP are crucial in providing protection against oxidative stress and potential disease prevention [51]. The anticancer properties, including inhibition of cancer cell growth and apoptosis induction, make them promising for cancer treatment and prevention [52]. The documented antimicrobial activity against various microorganisms, including bacteria and fungi, is a valuable asset in the fight against antimicrobial resistance,\u003cspan dir=\"RTL\"\u003e \u003c/span\u003eand requires further investigation. Additionally, select bromophenol exhibited anti-diabetic properties, regulating blood glucose levels and improving insulin sensitivity, highlighting their potential in diabetes management. These findings collectively underscore the need for comprehensive research to fully understand the mechanisms and clinical applications of these compounds [53].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Target prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSwiss target prediction web server possesses the ability to forecast the most plausible protein targets of small molecules, as stated by Gfeller et al. (2014). Exploring molecular targets holds significant value in identifying potential phenotypical adverse reactions or cross-reactivity arising from the action of marine-derived materials, such as BP. The findings for the top 25 targets are depicted in Figure 5. The likely binding sites for the compound are primarily linked to protease targets, family A G protein-coupled receptors, and enzymes that modulate drug responses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. MD Simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn MD simulation study was conducted to assess the stability of the \u003cem\u003eACE2\u003c/em\u003e receptor when complexed with BP [54]. The results of MD by GROMACS software were shown in figure 6. The Rg values reveal that the protein is more compact before docking compared to after docking, with a measurement of approximately 2.02 nm (Fig6,B).\u003c/p\u003e\n\u003cp\u003eThe SASA results illustrate that the solvent-accessible surface area of the protein was greater before docking compared to the post-docking state (Fig6, C).\u003c/p\u003e\n\u003cp\u003eExamining the RMSF of amino acids within the ligand\u0026apos;s active site indicates that ligand binding to the protein in the active site does not induce fluctuations in residues, maintaining the stability of the structure (Fig6, D).\u003c/p\u003e\n\u003cp\u003eIn terms of RMSD, the analysis indicates that the complex exhibits an approximate value of 0.19 nm, demonstrating optimization compared to the protein alone (Fig6, E).\u003c/p\u003e\n\u003cp\u003eThe results of Imod server was shown in Figure 7.The MD simulations examined the BP/\u003cem\u003e ACE2\u003c/em\u003e receptor complex, including Normal Mode Analysis (NMA), which calculates a molecule\u0026apos;s vibrational modes and provides insights into its flexibility and dynamics. Deformability Assessment gauges a molecule\u0026apos;s capacity for conformational changes, revealing its stability. Methods like B-Factor Visualization, Eigenvalue Determination, and Variance Representation analyze atomic mobility, collective motions, and flexibility, while Covariance Mapping explores correlated motions. Elastic Network Diagrams depict molecule stiffness and interactions, aiding in understanding structural stability and flexibility. Results are illustrated in Figure 7, including NMA in Figure 7a, the peaks in Figure 7b reveal the deformability of the respective molecule in Figure 7b. The empirical B-factor graphs, as shown in Figure 7c, were derived from the relevant PDB data and NMA mobility of the complexes. Figure 7d displays the computed eigenvalues of the docked complexes, which depict the rigidity of protein motion. The BP/\u003cem\u003eACE2 \u003c/em\u003ecomplex stands out for requiring the least energy to undergo structural deformation, as indicated by the lowest eigenvalue (8.897083e-05) compared to the \u0026beta;-D-mannose/\u003cem\u003eACE2\u003c/em\u003e complex. Figure 7e demonstrates the inverse relationship between eigenvalue and associated variance. Individual variance is shown with red color, while variance is depicted with green color. The interaction patterns between residue pairs are show cased in the co-variance map (Figure 7f), where red indicates correlated motion, white signifies uncorrelated motion, and blue denotes anti-correlated motion between residue pairs. The elastic network model, depicted in the elastic map (Figure 7g), illustrates inter-atomic connections using dots. The color gradient of these dots corresponds directly to their stiffness, with darker spots representing stiffer connections.\u003c/p\u003e"},{"header":"4. CONCLUSION","content":"\u003cp\u003eThe findings of this investigation underscore the considerable potential of BP sourced from marine origins as an innovative therapeutic agent, offering valuable insights into the intricate role of \u003cem\u003eACE2\u003c/em\u003e in circulatory hemostasis regulation and the prevention of \u003cem\u003eCovid-19\u003c/em\u003e viral entry. The comprehensive assessment of diverse parameters has revealed substantial enhancements compared to the control, affirming the robustness and feasibility of BP as a promising drug candidate.\u003c/p\u003e\n\u003cp\u003eThe elevated scores observed across a spectrum of critical parameters highlight the appropriateness of BP for further exploration and experimental validation. These discoveries unveil unexplored avenues for pharmaceutical research, particularly in the realm of circulatory modulation and the mitigation of the \u003cem\u003eCovid-19\u003c/em\u003e pandemic. While \u003cem\u003ein-silico\u003c/em\u003e studies lay the foundation, subsequent \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments are imperative to validate the efficacy and safety of BP as a viable treatment option.\u003c/p\u003e\n\u003cp\u003eThe progression of this finding from a theoretical concept to a tangible therapeutic solution will require multidisciplinary collaborative efforts among computational biologists, medicinal chemists, and virologists. This interdisciplinary approach is pivotal in advancing the understanding and application of BP, ultimately contributing to the development of innovative solutions for circulatory regulation and combating the challenges posed by the \u003cem\u003eCovid-19\u003c/em\u003e pandemic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interest or personal relationship that could influence this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eT. P. Sheahan and M. B. Frieman, \u0026quot;The continued epidemic threat of SARS-CoV-2 and implications for the future of global public health,\u0026quot; \u003cem\u003eCurrent Opinion in Virology, \u003c/em\u003evol. 40, pp. 37-40, 2020.\u003c/li\u003e\n\u003cli\u003eE. J. 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Seth, \u0026quot;A molecular dynamics simulation study of the ACE2 receptor with screened natural inhibitors to identify novel drug candidate against COVID-19,\u0026quot; \u003cem\u003ePeerJ, \u003c/em\u003evol. 9, p. e11171, 2021.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Angiotensin-Converting Enzyme 2 (ACE2), Bromophenol, Covid-19, Molecular Docking, Molecular Dynamic, Circulatory hemostasis. ","lastPublishedDoi":"10.21203/rs.3.rs-4017904/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4017904/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe possible recurrent threat of the \u003cem\u003eCOVID-19\u003c/em\u003epandemic driven by SARS-CoV-2 underscores the critical need for innovative pharmaceutical interventions targeting Angiotensin-Converting Enzyme 2 (\u003cem\u003eACE2) \u003c/em\u003e\u0026nbsp;receptors. Beyond the recognized role of \u003cem\u003eACE2\u003c/em\u003ein viral entry, its intricate involvement in circulatory hemostasis, with potential hypotension-related complications, necessitates a comprehensive approach. This \u003cem\u003ein silico\u003c/em\u003e study investigates the therapeutic potential of Bromophenol (BP) derived from Halophitys incurves (HIE) against both \u003cem\u003eACE2\u003c/em\u003e-mediated viral entry and circulatory complications, particularly hypotension.\u003c/p\u003e\n\u003cp\u003eUtilizing advanced \u003cem\u003ein silico\u003c/em\u003e techniques; we assessed the pharmacokinetic parameters of BP through SWISSADME, ADME/T, and Swisstargetprediction. The Molecular Dynamics Simulation analysis further substantiated the favorable interactions within the BP-\u003cem\u003eACE2 \u003c/em\u003e\u0026nbsp;complex. The results elucidated a favorable performance of BP in comparison to β-D-Mannose, serving as a potent inhibitor in impeding \u003cem\u003eACE2\u003c/em\u003e-mediated viral entry and contributing to the regulation of circulatory hemostasis. This inquiry emphasizes BP's potential as a robust inhibitor against the multifaceted actions of \u003cem\u003eACE2\u003c/em\u003e, offering valuable insights into its therapeutic effectiveness against \u003cem\u003eCOVID-19\u003c/em\u003e. Additionally, it contributes to a deeper understanding of \u003cem\u003eACE2-\u003c/em\u003emediated circulatory hemostasis by revealing BP's regulatory role in this physiological process. The encouraging findings warrant further exploration of BP as a novel therapeutic agent targeting \u003cem\u003eACE2\u003c/em\u003e -induced dual unfavorable actions.\u003c/p\u003e","manuscriptTitle":"Dual Action Potential: Bromophenol in Combatting COVID-19 and Modulating ACE2-Mediated Circulatory Hemostasis: A Molecular Modeling Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 08:12:28","doi":"10.21203/rs.3.rs-4017904/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6fe66072-2635-421f-9de2-9612b920f76c","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-24T22:14:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 08:12:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4017904","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4017904","identity":"rs-4017904","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}