Endothelial soluble guanylyl cyclase enzyme inhibitors as novel target for treatment of sepsis related hypotension

Endothelial soluble guanylyl cyclase enzyme inhibitors as novel target for treatment of sepsis related hypotension | 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 Endothelial soluble guanylyl cyclase enzyme inhibitors as novel target for treatment of sepsis related hypotension Yousif Ali Ahmed Suleiman, Yassir Almofti, abouzer Mohammed Khalil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5678666/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 Background Sepsis-related hypotension is a life-threatening condition due to systemic infection leading to widespread inflammation and blood vessel dilation. This can cause a dramatic drop in blood pressure, impairing blood flow to vital organs and potentially leading to organ failure and death. NO was recognized as a significant factor in sepsis in 1990 and became an important therapeutic target. NO plays a dual role in sepsis, exhibiting both beneficial and harmful effects. Inhibiting sGC may help reduce the excessive vasodilation associated with sepsis-induced vasoplegia. Methods This study utilized CADD to screen over 320 naturally occurring compounds from the PubChem database for potential sGC inhibitors. A comprehensive virtual screening process, which included protein-ligand docking, binding free energy calculations, and pharmacokinetic profiling, led to the identification of promising candidates such as Hypericin and Hypocrellin A2. Results These compounds demonstrated superior binding affinities and pharmacokinetic properties compared to existing inhibitors. Hypericin achieved a docking score of -14.232, indicating strong interactions with the receptor. It also exhibited favourable pharmacokinetic characteristics, including significant tissue-binding potential and stability within the binding pocket, as well as low predicted toxicity and a substantial safety margin. Conclusions This research lays the groundwork for future in vitro and in vivo studies, which could improve Hypericin-based effective therapies for sepsis-induced vasoplegia and hypotension. Bioinformatics Nitric Oxide receptor inhibitor Hypericin Sepsis Hypotension Computational drug design Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Sepsis is a complex immune mechanism and is described as a life-threatening dysfunction of vital organs caused by dysregulated host reactions to infections ( 1 , 2 ). Sepsis is further characterised by systemic inflammatory response syndrome (SIRS), which represents an excessive production of inflammatory cytokines that activate body immunity ( 3 ). Therefore, sepsis denotes a considerable clinical challenge associated with high mortality rates and substantial economic impact ( 4 ). Estimating sepsis-related death is complex because of the controversy in clinical definitions, heterogeneous clinical signs of patients, different hospital-related coding systems for sepsis, and varying dimensions of sepsis-related public awareness. Therefore, every year, sepsis and septic shock cause millions of deaths worldwide, posing a major health challenge ( 5 ). Septic shock is a critical sepsis state marked by low systemic vascular resistance, hypotension, high cardiac output and perfusion irregularities due to peripheral arteriolar vasodilation, resulting in harmful cellular and metabolic consequences with 50–75% mortality ( 6 , 7 ). According to the guideline of The Society of Critical Care Medicine’s Surviving Sepsis, systolic blood pressure of 100 mmHg or less is an indication of a quick Sequential Organ Failure Assessment score (qSOFA), which helps to identify patients with suspected sepsis infections ( 2 ). Since hypotension exacerbates tissue perfusion, it is plausible that certain organ injuries can be prevented by maintaining a suitable arterial pressure. Preventing hypotension is, therefore, a vital component of sepsis management ( 8 , 9 ). Excessive release of nitric oxide (NO), triggered by inducible nitric oxide synthase (iNOS), interleukin (IL)-1 and tumour necrosis factor (TNF), was identified as a crucial factor in sepsis-induced vascular relaxation hypotension in septic shock ( 10 – 13 ). Sequential binding of NO to its primary receptor soluble guanylate cyclase ( S GC) on target endothelial cell membranes boosted the function of S GC a hundred-fold, leading to intracellular cGMP-induced pathophysiology of vasoplegia, promoting vasorelaxation ( 14 – 16 ). In addition, activating S GC in mice with severe sepsis showed reduced neutrophil migration and increased leukocyte sequestration into lung parenchyma tissues, culminating in lung failure and sepsis pneumonia in mice. Consequently, S GC inhibition reduced the leukocyte sequestration with partially restoring the neutrophil accumulation into the pulmonary parenchyma ( 17 ). In addition, inhibiting S GC during the later stage of sepsis mitigated the excessive vasodilation link to sepsis-induced vasoplegia, resulting in decreased mortality in mice ( 18 ). The iNOS-dependent suppression of S GC has also been documented as a protective mechanism against TNF-induced fatal shock, bradycardia, and hypotension ( 19 ). However, developing S GC inhibitors faces challenges in finding specific compounds with favourable pharmacokinetics and low toxicity. Recently, computer-aided drug design (CADD) has facilitated the efficient identification of potential inhibitors from large chemical libraries, proving effective in various therapeutic areas. In this study, we employed CADD to screen over 320 naturally occurring compounds from the PubChem database for potential S GC inhibitors. Using a comprehensive virtual screening pipeline that included protein-ligand docking, binding free energy calculations, and pharmacokinetic profiling, we identified promising candidates like Hypericin and Hypocrellin A2, which showed better binding affinities and pharmacokinetic properties than existing inhibitors. Hypericin (4,5,7,4′,5′,7′-hexahydroxy-2,2′-dimethylnaphtodianthrone) and Hypocrellin A2 (3,10-xylene-4,9-anthracene derivative) are naturally occurring compounds which have broad pharmacological spectrum ( 20 , 21 ). Hypericin achieved an impressive docking score of -14.232, indicating strong receptor interactions and compliance with Lipinski’s rule of five, suggesting high drug-likeness. It also displayed favourable pharmacokinetic traits, including high tissue-binding potential and stability in the binding pocket, along with low predicted toxicity and a high safety margin. The findings highlight hypericin as a promising therapeutic candidate for overcoming the limitations of conventional inhibitors. This study provides a foundation for further in vitro and in vivo investigations, with the potential to advance effective treatments for sepsis-induced vasoplegia. Methods Ligand Selection and Preparation This study utilized standardized techniques for the preparation and analysis of ligands. Hypericin, the active drug, and a reference SGCE inhibitor were chosen based on their documented efficacy. Their two-dimensional structures were obtained from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) or shown utilizing MarvinSketch 22.13 and the 2D sketching module of Schrödinger Maestro 11.5. Ligands were generated utilizing Maestro's LigPrep module with the OPLS3 force field to ensure precise ionization states at physiological pH (7.2 ± 0.2). This process assured molecular integrity for subsequent analysis. Protein Preparation The three-dimensional structure of the SGCE enzyme was sourced from the Protein Data Bank (PDB), selected for its high resolution and well-defined active site to facilitate dependable docking investigations. Protein preparation was conducted using the Protein Preparation Wizard in Schrödinger Maestro 11.5, with default parameters for accuracy. The refinement involved adjusting protonation states using PROPKA, eliminating water molecules situated more than 3.0 Å from heteroatoms, and conducting energy minimization with the OPLS3 force field to optimize the protein structure for docking. Receptor Grid Generation The receptor grid for the SGCE binding site was generated via the Receptor Grid Generation tool in Schrödinger Maestro. The grid was focused on the enzyme's active site, specified by the coordinates X = 22.831, Y = 36.407, and Z = 17.024, which align with the established binding pocket. These factors facilitated the accurate localization of the docking point. The resultant grid file was the basis for in-silico docking tests performed using AutoDock Vina. Molecular Docking Molecular docking was performed via AutoDock Vina, commencing with High-Throughput Virtual Screening (HTVS), which included adjustable ligand sampling to find prospective candidates. The highest-ranked compounds from HTVS underwent Standard Precision (SP) docking using flexible ligand sampling. Thereafter, Extra Precision (XP) docking with fixed ligand sampling (refinement alone) was conducted to enhance ligand-receptor interactions. The final selections for further analysis were determined by docking scores and the quality of binding conformations. Binding Free Energy Calculation The stability of SGCE-ligand complexes was assessed using the Molecular Mechanics Generalized Born Surface Area (MM/GBSA) technique. Calculations were conducted using the Prime MM-GBSA module in Schrödinger Maestro 11.5, utilizing the OPLS3 force field and the VSGB solvent model. Default parameters were employed for supplementary configurations. The computations provide quantitative insights into the binding efficiency and stability of the complexes, enabling the selection of the most stable interactions. Pharmacophore Modelling The critical pharmacophore characteristics associated with the interaction between Hypericin and the SGCE active site were determined by developing an e-pharmacophore hypothesis utilizing the Phase module in Schrödinger Maestro 11.5. This method facilitated the visualization of important pharmacophoric features, including hydrogen bond donors, acceptors, and hydrophobic areas, which are crucial for the design and assessment of prospective SGCE inhibitors. ADMET Assessment (Pharmacokinetic Analysis) The ADMET profiles of Hypericin and the reference SGCE inhibitor were assessed using SwissADME ( http://www.swissadme.ch ) and PROTOX-II ( https://tox-new.charite.de/protox_II/ ). This in silico investigation evaluated essential pharmacokinetic characteristics, including bioavailability, drug-likeness, and toxicity. The findings provide insights into the appropriateness of Hypericin as a prospective pharmacological candidate for SGCE inhibition, facilitating the enhancement of its therapeutic profile. Results This study found a collection of naturally occurring SGCE inhibitors from the PubChem database, consisting of more than 320 candidate chemicals. Of them, six exhibited substantial binding affinities for the SGCE enzyme. The substances comprised Hypericin, Hypocrellin A2, Stictic Acid, Viola Styrene, and a standard reference ligand from the database. The reference ligand, chosen for its established efficacy, demonstrated a Ligand Efficiency Value (LVS) score of -14.232, which acted as a standard for assessing the binding efficiency of the other ligands, as seen in Figure 1. After receptor-ligand interactions, a significant increase in the total polar surface area (TPSA) was noted for the conventional ligand, along with an enhanced Ligand Efficiency Value (LVS) score of -14.03, as shown in Table 1. This increased negativity signifies a decreased binding energy need and heightened receptor affinity, suggesting a strong interaction. Moreover, the elevated ligand concentration next to the receptor indicates a strong likelihood of tissue-protein interaction, which may result in a substantial volume of distribution and an extended elimination half-life (t½). Hypocrellin A2 has emerged as a viable candidate, with a high SlogP value of 4, which indicates moderate water solubility and an ideal equilibrium between hydrophilicity and lipophilicity. These characteristics improve its plasma bioavailability and promote effective renal excretion, perhaps resulting in a reduced elimination half-life (t½). Upon binding, Hypocrellin A2 exhibited a notable radial plot rise from 546.5 to 646.5, with an LVS score of -12.953, somewhat inferior to the standard ligand by about 2 points. Notwithstanding this little variation, the substantial binding capacity designates Hypocrellin A2 as a compelling subject for more research. Hypericin, a naturally occurring chemical (PubChem ID: 3663), demonstrated unique pharmacokinetic properties. It was characterized by a narrow radial plot, high molecular weight, poor lipophilicity, and increased TPSA, signifying its predominant localization inside the intravascular compartment. This restricted dispersion is associated with a reduced elimination half-life and a decreased volume of distribution. Notwithstanding these limitations, its Ligand Efficiency Value (LVS) score of -14, akin to that of the control ligand, underscores its promise as a viable option for subsequent in vitro and in vivo assessments. A thorough investigation demonstrated that AG2P1001 and Hypericin had the most advantageous binding interactions and stability, as seen in Fig. 2 . AG2P1001 (control ligand) exhibited strong binding affinity with an MM/GBSA score of -38.75. Hypericin demonstrated exceptional stability, achieving an MM/GBSA score of -40.36, so it is established as an optimal choice for in vitro and in vivo investigations. Hypocrellin A2 exhibited significant binding (Fig. 3 ), scoring − 37.44. However, it was less effective than AG2P1001 and Hypericin. Conversely, Stictic Acid and Viola Styrene demonstrated diminished interactions, with MM/GBSA values of -32.76 and − 34.62, respectively, highlighting their restricted efficacy as inhibitors. Table 1: Comparative Analysis of Physicochemical Properties, Binding Scores, and MM/GBSA Values of Selected Ligands in Docked and Undocked States. Ligand Slog-P TPSA LVS Score MM/GBSA AG2P1001 (Undocked) -16 114.9 - - AG2P1001 (Docked) -16 3149 -14.03 -38.75 Hypocrellin A2 (Undocked) 4 148.8 - - Hypocrellin A2 (Docked) 4 148.8 -12.953 -37.44 Stictic Acid (Undocked) 2.7 128.6 - - Stictic Acid (Docked) 2.7 128.6 -8.817 -32.76 Viola Styrene (Undocked) 3.6 38.7 - - Viola Styrene (Docked) 16 38.7 -8.28 -34.62 Hypericin (Undocked) 5.6 155.5 - - Hypericin (Docked) 5.6 155.5 -14.351 -40.36 The toxicity profile of Hypericin suggests the ability to interact with cytochrome P450 (CYP) enzymes, namely CYP1A2, CYP2C9, and CYP2C19, which may affect pharmacokinetics and drug-drug interactions. Moderate interactions with CYP3A4 and CYP2D6 were noted. Hypericin possesses a projected LD50 of 1000 mg/kg (Toxicity Class 4, GHS), indicating little overall toxicity. It demonstrates little nephrotoxicity, mild neurotoxicity, and carcinogenicity hazards, underscoring its potential as a safe and effective therapeutic option for further advancement. An extensive investigation of bond interactions was performed to assess the protein-ligand complex further, as seen in Fig. 4 . The hydrogen bond characteristics, comprising distances (H-A and D-A) and donor angles, were analyzed to evaluate their strength and orientation. Reduced H-A distances, exemplified by the 2.45 Å between LYS and the ligand, signify strong hydrogen bonding, which substantially enhances the stability of the complex. This comprehensive research offers essential insights into the interaction dynamics inside the active site. Essential residues substantially enhance binding stability. ASN 451 and LYS 471 function as donors, exhibiting robust contacts marked by advantageous angles and short H-A distances of 2.83 Å and 2.45 Å, respectively, as outlined in Table 2 . These factors underscore their crucial function in enhancing binding affinity. In contrast, ASP 458 and GLU 473 function as acceptors, with D-A lengths of roughly 3.9 Å, which suggests moderate hydrogen bonding strength. These findings emphasize the intricate interaction of residues within the binding contact. The binding interface study indicates that THR 474 establishes two hydrogen bonds with the ligand but at marginally extended distances, which may introduce flexibility to the binding site. This flexibility may affect the firmness of the entire complex. The reduced distances and advantageous angles of residues like ASN 451 and LYS 471 suggest strong hydrogen bonding, enhancing binding affinity and complex stability. These interactions together highlight a persistent binding location, therefore increasing the ligand's functional efficiency within the protein's active pocket. Table 2 Key Hydrogen Bond Interactions Between Protein Residues and Hypericin Ligand in the SGC Enzyme Complex. Interacting residues Bond Distance (A) Donor Angle Donor Type Acceptor Type ASN451 2.83 131.26 Nam O3 ASP458 3.55 126.96 O3 O- LYS471 2.45 142.67 N3+ O3 GLU473 2.70 147.35 O3 O- THR474 3.17 130.47 O3 O2 THR474 3.47 126.29 O3 O2 The iMODS server utilizes Normal Mode Analysis (NMA) to investigate collective movements in macromolecules, facilitating the evaluation of structural flexibility in cellular environments. NMA computes frequencies related to motion magnitudes and deformation vectors that signify atomic displacement directions, which is crucial for assessing molecular adaptability. Figure 5 A displays the findings of molecular dynamics (MD) simulations and normal mode analysis (NMA) for the Hypericin/sGC enzyme combination. Flexible zones were found by repeatedly altering the structure along low-energy modes and optimizing the root mean square deviation (RMSD). Peaks in deformability graphs (Fig. 5 B) signify flexible regions such as hinges, whereas lower values represent stiff sections. The B-factor, obtained via Normal Mode Analysis (NMA), measures the relative magnitude of atomic displacements in the Hypericin-sGC complex around its equilibrium conformation. Figure 5 D illustrates B-factor graphs that correlate the mobility of the complex with Protein Data Bank (PDB) RMSD values. The eigenvalues of normal modes, which signify structural stiffness, are directly associated with the energy necessary for deformation. Reduced eigenvalues indicate more flexibility, with the Hypericin-sGC complex exhibiting a major mode eigenvalue of 1.921427e-04, reflecting minimal energy requirements for deformation and highlighting its structural integrity (Fig. 5 C). The Elastic Network Model (ENM), seen in Fig. 5 G, illustrates the interatomic connections within the sGC structure, highlighting their significance in preserving structural integrity. Atomic bonds are illustrated as springs, with stiffness levels indicated by colour: darker grey denotes increased rigidity, whilst lighter hues reflect flexibility. This research showed areas of increased stiffness, which contribute to the protein's stability. Both flexible and stiff sections maintained a steady equilibrium throughout the simulation, hence affirming the structural integrity of the sGC enzyme. Figure 5 E presents a variance panel that delineates the distribution of variation across modes, with purple bars representing the variance of individual components and green bars indicating cumulative variance, therefore emphasizing each mode's contribution to macromolecular motion. The covariance matrix examines the motions of residue pairs, where correlated movements (red) signify synchronized motion, uncorrelated movements (white) denote independent motion, and anti-correlated movements (blue) imply opposing directional movement. These patterns are crucial for comprehending the dynamic connections and functional links within various areas of the sGC enzyme. These findings provide essential insights into the dynamic behavior and stability of the Hypericin-sGC enzyme combination. The strong interactions between Hypericin and the sGC enzyme underscore its potential as a reliable and powerful inhibitor. This stability may significantly contribute to the restoration of cellular homeostasis and the alleviation of pathological processes linked to sepsis and similar diseases, indicating its potential as a therapeutic agent. Discussions Sepsis-induced vasoplegia is a critical and potentially fatal consequence of sepsis, principally caused by excessive production of NO. This syndrome results in considerable vasodilation, compromised vascular tone, and multi-organ failure, highlighting the necessity for novel treatment approaches ( 22 – 24 ). The restricted effectiveness of existing therapies underscores the necessity for focused approaches. This work utilized computational drug design tools to uncover effective inhibitors of sGC enzyme, a crucial enzyme in the nitric oxide-mediated vasodilation pathway. After screening more than 320 natural sGC inhibitors, Hypericin and Hypocrellin A2 were identified as intriguing candidates with significant binding affinities. Computational Drug Design Methodology The virtual screening technique utilized a thorough methodology encompassing protein and ligand preparation, binding site evaluation, and stratified docking methods, including High-Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP). These approaches precisely forecasted interactions between bioactive substances and sGC. Docking scores and binding energy indicated that lead compounds had better binding affinities than established inhibitors. This strategy, aligned with previous research revealing c-Myc inhibitors, highlights the dependability of computational methods in drug development, further validated by employing several docking approaches ( 25 , 26 ) Binding Free Energy Analysis We conducted Molecular Mechanics with Generalized Born and Surface Area (MMGBSA) binding free energy calculations to confirm docking results for ligand-enzyme complexes. Hypericin and Hypocrellin A2 exhibited significant interactions between sGC and Hypericin, displaying low binding energy and underscoring their therapeutic potential. The MMGBSA approach accurately assesses binding affinities by partitioning free energy into van der Waals, electrostatic, and solvation contributions. This method, averaged across molecular dynamics snapshots, demonstrates Hypericin's strong affinity via hydrogen bonding and hydrophobic interactions, emphasizing the significance of computational techniques in drug discovery ( 27 ). Pharmacokinetic Properties Hypericin demonstrated favourable pharmacokinetic characteristics, such as significant tissue-binding capacity and a prolonged half-life. The augmented total polar surface area upon receptor engagement improves its stability and dispersion, hence enhancing its therapeutic potential. In contrast, Hypocrellin A2 exhibited advantageous water solubility (logP = 4), enhancing bioavailability and facilitating effective renal clearance. These findings correspond with previous research emphasizing the beneficial absorption and distribution properties of natural substances. Furthermore, Hypericin's compliance with Lipinski's rule of five highlights its drug-like properties, providing a significant advantage over traditional inhibitors that frequently violate these standards. Toxicity Assessment Metabolic pathways can significantly influence a drug's bioavailability and half-life, leading to differences in therapeutic effectiveness. The metabolic profile of Hypericin, a principal ingredient in St. John's Wort (Hypericum perforatum), highlights its substantial interactions with several cytochrome P450 (CYP) enzymes. It was reported that many CYP enzymes participate in metabolising several drugs ( 28 ), considering that CYP enzymes affect Hypericin's pharmacokinetics and possible therapeutic uses ( 29 ). The active and inactive interaction clusters of Hypericin in this study indicate significant interactions with CYP1A2, CYP2C9, and CYP2C19 (Fig. 6), indicating unique metabolic pathways essential for comprehending its therapeutic potential and a considerable probability of biotransformation of Hypericin through these enzymes. The co-administration of Hypericin with pharmaceuticals metabolized by CYP enzymes may modify the plasma concentrations of the concomitant medications, potentially reducing effectiveness or exacerbating side effects. Prior research indicates that St. John's Wort extracts containing Hypericin can suppress the activities of CYP1A2 and CYP2C9, heightening concerns about its pharmacokinetic interactions with other drugs ( 30 , 31 ). Furthermore, considerable interaction probabilities with CYP3A4 and CYP2D6 indicate probable but clinically significant alterations in drug metabolism. Although Hypericin does not markedly stimulate CYP3A4 activity, its possible effect on the metabolism of CYP3A4 substrates necessitates vigilant monitoring during simultaneous drug administration ( 29 , 32 ). The toxicological assessment of Hypericin indicates a projected LD50 of 1000 mg/kg, categorizing it under Toxicity Class 4 according to the Globally Harmonized System (GHS). This classification, signifying a reduced toxicity profile compared to more dangerous compounds, highlights a comparatively broad therapeutic window While underscoring the importance of careful dosage monitoring to reduce side effects. The low likelihood of neurotoxicity and carcinogenicity further supports Hypericin's safety for therapeutic use. Moreover, the limited toxicological hazards to cardiac and immunological processes enhance its potential in fields like cancer, where safety profiles are paramount. Notwithstanding these encouraging outcomes, ongoing study is essential. Thorough experimental validation is necessary to clarify the mechanisms underlying Hypericin's interactions with CYP enzymes, and additional investigation into the clinical ramifications of these interactions is crucial to guarantee patient safety and therapeutic effectiveness as Hypericin becomes increasingly significant in pharmacological research. Conclusion This study highlights the potential of natural chemicals in drug development and the crucial role of computational methods in identifying prospective therapeutic agents. Here, we identify Hypericin as a promising therapeutic agent for treating sepsis-induced vasoplegia and hypotension by inhibiting the sGC enzyme and reducing multi-organ failure. Moreover, Hypericin's natural origin and pharmacological characteristics provide a twofold benefit—novel therapeutic potential and fewer toxicity concerns than conventional medicines. Therefore, this research enhances the comprehension of sepsis treatment options and underscores the incorporation of computational drug design into modern pharmacology. Nonetheless, in vitro and in vivo validations are essential to verify these computational findings and to evaluate Hypericin's therapeutic potential thoroughly. These contributions substantially enhance current discussions on sepsis therapy, facilitating future advancements in addressing this life-threatening illness. Utilizing natural chemicals such as Hypericin, future pharmaceutical strategies may effectively and safely target complicated conditions like sepsis. Declarations Funding: N/A Acknowledgement We deeply thank Abuzor Mohamed Mohyeldin Khalil for his invaluable guidance and expertise throughout this study. We also sincerely thank Dr Fatema and her colleagues for their meticulous editing and insightful feedback. Conflict of Interest: The author declared no conflict of interest. Corresponding Author: Yassir Ali Almofti Ahmed Department of Molecular Biology & Bioinformatics, College of Veterinary Medicine, University of Bahri Phone number: +249901589515; Email: [email protected] Orchid ID: 0000-0002-7174-8417 Authors: Yousif Ali Ahmed Suleiman Al Adan hospital, Kuwait City, Hadiah Area, Al Ahmade province Phone number: +0096597550690; Email: [email protected] Orchid ID: 0009-0003-2606-386X Abuzor Mohamed Mohyeldin Khalil Institute of Endemic Diseases, University of Khartum, Sudan Phone number: +971501129550; Email: [email protected] Orchid ID: Author contributions : Y.A.A.A. is the principal investigator and the main supervisor. Y.A.A.S. formulated the research idea, screened the natural drug from PubChem, defined the control ligand and the structure of the tested receptor, performed all the experiments, evaluated the data and prepared the final manuscript. A.M.M.K. helped in the experimental procedures and data analysis to prepare the main manuscript. 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J Pharmacol Exp Ther 294(1):88–95 Henderson L, Yue Q, Bergquist C, Gerden B, Arlett P (2002) St John's wort (Hypericum perforatum): drug interactions and clinical outcomes. Br J Clin Pharmacol 54(4):349–356 Hakkola J, Hukkanen J, Turpeinen M, Pelkonen O (2020) Inhibition and induction of CYP enzymes in humans: an update. Arch Toxicol 94(11):3671–3722 Additional Declarations The authors declare no competing interests. Supplementary Files Figure4A.pdf Hypericin binding Figure5A.pdf Molecular simulation Figure5B.pdf Deformability Figure5C.pdf Eigen values Figure5D.pdf B-factor Figure5E.pdf Individual variation Figure5F.pdf Covariance Figure5G.pdf Covariance Figure6.pdf Probability GA.png 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. 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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-5678666","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392495819,"identity":"897107f1-43ba-4d42-a9e6-963dc1cd28f5","order_by":0,"name":"Yousif Ali Ahmed Suleiman","email":"","orcid":"https://orcid.org/0009-0003-2606-386X","institution":"Al Adan hospital Kuwait department of internal medicine","correspondingAuthor":false,"prefix":"","firstName":"Yousif","middleName":"Ali Ahmed","lastName":"Suleiman","suffix":""},{"id":392495820,"identity":"3c8a3918-9264-4d4c-a8fe-0cdfb2058c83","order_by":1,"name":"Yassir Almofti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYLCCCgYJOX72BiDLwIJILWcSJIwlew6AtEgQrYUhccONBBCTCC0Gx08nPjj4wyJxw83nVzf8KJBg4G/vTsCv5UzuZoMDQIfNvJ1TdrMH6DCJM2c34NdyIHeb9IcECdm+2zlpN3iAWgwkcgloOf92mwTQFsaGm2fSbv4hSsuNXLAWxQk32I/dJsoWyRtvgX5JAwVyDtttGQMJHoJ+4Tufu/HBAZs6YFQef3bzzR8bOf72XvxaFA7AmTwGYBKvchCQb4Az2R8QVD0KRsEoGAUjEwAA5exQb2RiIiQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7174-8417","institution":"Department of Biomedical Sciences, College of Veterinary Medicine, King Faisal University, Al Ahsa, 31982, Saudi Arabia","correspondingAuthor":true,"prefix":"","firstName":"Yassir","middleName":"","lastName":"Almofti","suffix":""},{"id":392496198,"identity":"ba2f0c9d-074d-4890-b7fe-cf47e098d58e","order_by":2,"name":"abouzer Mohammed Khalil","email":"","orcid":"","institution":"University of Khartoum","correspondingAuthor":false,"prefix":"","firstName":"abouzer","middleName":"Mohammed","lastName":"Khalil","suffix":""}],"badges":[],"createdAt":"2024-12-19 17:13:41","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-5678666/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5678666/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71967780,"identity":"3359f80f-1b17-4131-b126-305370f13c62","added_by":"auto","created_at":"2024-12-20 07:44:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47201,"visible":true,"origin":"","legend":"\u003cp\u003eStructures of the lead compounds\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/3a4828eda140391d042be07c.png"},{"id":71969211,"identity":"01720a36-325e-47b0-9806-fa831180c046","added_by":"auto","created_at":"2024-12-20 08:00:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":405980,"visible":true,"origin":"","legend":"\u003cp\u003eThe 3D structure of the SGCE enzyme complexed with the Hypericin molecule in its active site is shown. A golden-hued molecule, situated in the core of the binding site, is distinguished by many aromatic rings. The binding study indicated the existence of several hydrogen bonds and weak electrostatic interactions, which together enhance the stability and specificity of the ligand-enzyme complex.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/94fe431d59299b04497c449a.png"},{"id":71967787,"identity":"0373921d-849f-49f2-919e-f87b5b68b9d9","added_by":"auto","created_at":"2024-12-20 07:44:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":473579,"visible":true,"origin":"","legend":"\u003cp\u003eThe 3D configuration of the SGCE enzyme complexed with the Hypocrellin molecule in its active region is shown. Hypocrellin, distinguished by many aromatic rings, is located near the beta-pleated sheet region of the enzyme. The binding contacts consist of many hydrogen bonds and weak electrostatic forces, shown in blue, which together stabilize the ligand-enzyme complex and underscore its potential as an effective SGCE inhibitor.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/a11986af8ccb519601581d40.png"},{"id":71968114,"identity":"6f4d5ce1-acfc-476f-83eb-3e91fdfe1dd7","added_by":"auto","created_at":"2024-12-20 07:52:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":215246,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking interactions demonstrating the stabilization of protein-ligand complexes. (A) Hypericin is coupled to the sGC enzyme, with hydrogen bond interactions shown as blue lines, highlighting essential stabilizing factors. (B) Hypocrellin A2 associated with the sGC enzyme, emphasizing analogous hydrogen bond interactions (blue lines) that enhance binding affinity and complex stability. The illustration highlights the significance of hydrogen bonding in preserving the structural integrity and stability of the complexes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/a9ce86040740d3289e9689ca.png"},{"id":71969213,"identity":"703d07ee-1acb-4caa-bae7-b40fe4585987","added_by":"auto","created_at":"2024-12-20 08:00:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":353221,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular dynamics simulation outcomes for the Hypericin-SGCE Complex, evaluated utilizing the iMODS service. \u003cstrong\u003ePanel A\u003c/strong\u003eillustrates the stability generated from NMA, emphasizing the flexibility within the complex. \u003cstrong\u003ePanel B\u003c/strong\u003e illustrates deformability, highlighting areas susceptible to structural alterations. \u003cstrong\u003ePanel C\u003c/strong\u003edelineates eigenvalues, signifying the degree of collective motion. \u003cstrong\u003ePanel D\u003c/strong\u003edisplays B-factors, indicating atomic-level variations. \u003cstrong\u003ePanel E\u003c/strong\u003edepicts variation, with purple bars representing individual contributions and green bars indicating cumulative effects. \u003cstrong\u003ePanel F\u003c/strong\u003edisplays the covariance matrix, classifying residue movements as correlated (red), uncorrelated (white), or anti-correlated (blue). \u003cstrong\u003ePanel G\u003c/strong\u003eillustrates the elastic network, highlighting atomic connection and structural integrity.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/9f649b371e9e3cf5e9e35a7f.png"},{"id":71968122,"identity":"c511ad18-06b3-4cc5-b4ad-3b7eac60b942","added_by":"auto","created_at":"2024-12-20 07:52:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":54951,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork chart of Hypericin’s predicted toxicological profiles\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/827b0a40b220f25bea9e3ba0.png"},{"id":71970535,"identity":"aa517b69-b6cc-4cc7-9d45-911dd5319cea","added_by":"auto","created_at":"2024-12-20 08:16:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2142487,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/dab7e6aa-df41-4877-bee0-b359cafc6975.pdf"},{"id":71967779,"identity":"37ac5298-4f1e-4013-beda-3e92cfe4bda5","added_by":"auto","created_at":"2024-12-20 07:44:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45335,"visible":true,"origin":"","legend":"\u003cp\u003eHypericin binding\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure4A.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/62c0c95c58a251ad34faa96e.pdf"},{"id":71967785,"identity":"16145dcc-ac9a-4e5c-9af0-a438f858434b","added_by":"auto","created_at":"2024-12-20 07:44:37","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":126733,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular simulation\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure5A.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/41bece258b126d2a6860f19a.pdf"},{"id":71969859,"identity":"6c8cfc0b-044f-4261-8038-6859d0502d91","added_by":"auto","created_at":"2024-12-20 08:08:51","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":41646,"visible":true,"origin":"","legend":"\u003cp\u003eDeformability\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure5B.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/474df46203dc722bd92c9a2e.pdf"},{"id":71967783,"identity":"17609fb5-adea-4719-9419-929ec6e0c288","added_by":"auto","created_at":"2024-12-20 07:44:37","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":47050,"visible":true,"origin":"","legend":"\u003cp\u003eEigen values\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure5C.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/d59b3358fdd47d1a2391ecfe.pdf"},{"id":71967791,"identity":"7421d1f6-34d1-40d4-a46a-5d4057385938","added_by":"auto","created_at":"2024-12-20 07:44:37","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":42131,"visible":true,"origin":"","legend":"\u003cp\u003eB-factor\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure5D.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/bcec230b8b70bdfde90b3170.pdf"},{"id":71969849,"identity":"f536c299-a876-4b25-ab21-8da3d5b7c959","added_by":"auto","created_at":"2024-12-20 08:08:49","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":41049,"visible":true,"origin":"","legend":"\u003cp\u003eIndividual variation\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure5E.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/5855e7ef4045c4e78e6f0283.pdf"},{"id":71967798,"identity":"95dddf47-357a-4695-8cf1-430c8c815c36","added_by":"auto","created_at":"2024-12-20 07:44:37","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":118605,"visible":true,"origin":"","legend":"\u003cp\u003eCovariance\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure5F.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/72d2de3456a61da22a00fa54.pdf"},{"id":71969851,"identity":"6322835b-3c3a-4e4c-80a3-34fb3b205f90","added_by":"auto","created_at":"2024-12-20 08:08:50","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":65154,"visible":true,"origin":"","legend":"\u003cp\u003eCovariance\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure5G.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/a5bd1db1b57cd312faeda154.pdf"},{"id":71967814,"identity":"942a1431-e5ce-4741-be9d-7fe2febdfabc","added_by":"auto","created_at":"2024-12-20 07:44:38","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":51291,"visible":true,"origin":"","legend":"\u003cp\u003eProbability\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/e196f92658ad645b48835e3a.pdf"},{"id":71967801,"identity":"29f4abb5-bb8d-44c9-ac44-79154db85bf2","added_by":"auto","created_at":"2024-12-20 07:44:37","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":126452,"visible":true,"origin":"","legend":"","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-5678666/v1/123251c90fb34b26bd6f9d0f.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEndothelial soluble guanylyl cyclase enzyme inhibitors as novel target for treatment of sepsis related hypotension","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis is a complex immune mechanism and is described as a life-threatening dysfunction of vital organs caused by dysregulated host reactions to infections (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Sepsis is further characterised by systemic inflammatory response syndrome (SIRS), which represents an excessive production of inflammatory cytokines that activate body immunity (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, sepsis denotes a considerable clinical challenge associated with high mortality rates and substantial economic impact (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Estimating sepsis-related death is complex because of the controversy in clinical definitions, heterogeneous clinical signs of patients, different hospital-related coding systems for sepsis, and varying dimensions of sepsis-related public awareness. Therefore, every year, sepsis and septic shock cause millions of deaths worldwide, posing a major health challenge (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeptic shock is a critical sepsis state marked by low systemic vascular resistance, hypotension, high cardiac output and perfusion irregularities due to peripheral arteriolar vasodilation, resulting in harmful cellular and metabolic consequences with 50\u0026ndash;75% mortality (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). According to the guideline of The Society of Critical Care Medicine\u0026rsquo;s Surviving Sepsis, systolic blood pressure of 100 mmHg or less is an indication of a quick Sequential Organ Failure Assessment score (qSOFA), which helps to identify patients with suspected sepsis infections (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Since hypotension exacerbates tissue perfusion, it is plausible that certain organ injuries can be prevented by maintaining a suitable arterial pressure. Preventing hypotension is, therefore, a vital component of sepsis management (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Excessive release of nitric oxide (NO), triggered by inducible nitric oxide synthase (iNOS), interleukin (IL)-1 and tumour necrosis factor (TNF), was identified as a crucial factor in sepsis-induced vascular relaxation hypotension in septic shock (\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Sequential binding of NO to its primary receptor soluble guanylate cyclase (\u003csub\u003eS\u003c/sub\u003eGC) on target endothelial cell membranes boosted the function of \u003csub\u003eS\u003c/sub\u003eGC a hundred-fold, leading to intracellular cGMP-induced pathophysiology of vasoplegia, promoting vasorelaxation (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In addition, activating \u003csub\u003eS\u003c/sub\u003eGC in mice with severe sepsis showed reduced neutrophil migration and increased leukocyte sequestration into lung parenchyma tissues, culminating in lung failure and sepsis pneumonia in mice. Consequently, \u003csub\u003eS\u003c/sub\u003eGC inhibition reduced the leukocyte sequestration with partially restoring the neutrophil accumulation into the pulmonary parenchyma (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In addition, inhibiting \u003csub\u003eS\u003c/sub\u003eGC during the later stage of sepsis mitigated the excessive vasodilation link to sepsis-induced vasoplegia, resulting in decreased mortality in mice (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The iNOS-dependent suppression of \u003csub\u003eS\u003c/sub\u003eGC has also been documented as a protective mechanism against TNF-induced fatal shock, bradycardia, and hypotension (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, developing \u003csub\u003eS\u003c/sub\u003eGC inhibitors faces challenges in finding specific compounds with favourable pharmacokinetics and low toxicity. Recently, computer-aided drug design (CADD) has facilitated the efficient identification of potential inhibitors from large chemical libraries, proving effective in various therapeutic areas.\u003c/p\u003e \u003cp\u003eIn this study, we employed CADD to screen over 320 naturally occurring compounds from the PubChem database for potential \u003csub\u003eS\u003c/sub\u003eGC inhibitors. Using a comprehensive virtual screening pipeline that included protein-ligand docking, binding free energy calculations, and pharmacokinetic profiling, we identified promising candidates like Hypericin and Hypocrellin A2, which showed better binding affinities and pharmacokinetic properties than existing inhibitors. Hypericin (4,5,7,4\u0026prime;,5\u0026prime;,7\u0026prime;-hexahydroxy-2,2\u0026prime;-dimethylnaphtodianthrone) and Hypocrellin A2 (3,10-xylene-4,9-anthracene derivative) are naturally occurring compounds which have broad pharmacological spectrum (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Hypericin achieved an impressive docking score of -14.232, indicating strong receptor interactions and compliance with Lipinski\u0026rsquo;s rule of five, suggesting high drug-likeness. It also displayed favourable pharmacokinetic traits, including high tissue-binding potential and stability in the binding pocket, along with low predicted toxicity and a high safety margin. The findings highlight hypericin as a promising therapeutic candidate for overcoming the limitations of conventional inhibitors. This study provides a foundation for further in vitro and in vivo investigations, with the potential to advance effective treatments for sepsis-induced vasoplegia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLigand Selection and Preparation\u003c/h2\u003e \u003cp\u003eThis study utilized standardized techniques for the preparation and analysis of ligands. Hypericin, the active drug, and a reference SGCE inhibitor were chosen based on their documented efficacy. Their two-dimensional structures were obtained from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) or shown utilizing MarvinSketch 22.13 and the 2D sketching module of Schr\u0026ouml;dinger Maestro 11.5. Ligands were generated utilizing Maestro's LigPrep module with the OPLS3 force field to ensure precise ionization states at physiological pH (7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2). This process assured molecular integrity for subsequent analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProtein Preparation\u003c/h3\u003e\n\u003cp\u003eThe three-dimensional structure of the SGCE enzyme was sourced from the Protein Data Bank (PDB), selected for its high resolution and well-defined active site to facilitate dependable docking investigations. Protein preparation was conducted using the Protein Preparation Wizard in Schr\u0026ouml;dinger Maestro 11.5, with default parameters for accuracy. The refinement involved adjusting protonation states using PROPKA, eliminating water molecules situated more than 3.0 \u0026Aring; from heteroatoms, and conducting energy minimization with the OPLS3 force field to optimize the protein structure for docking.\u003c/p\u003e\n\u003ch3\u003eReceptor Grid Generation\u003c/h3\u003e\n\u003cp\u003eThe receptor grid for the SGCE binding site was generated via the Receptor Grid Generation tool in Schr\u0026ouml;dinger Maestro. The grid was focused on the enzyme's active site, specified by the coordinates X\u0026thinsp;=\u0026thinsp;22.831, Y\u0026thinsp;=\u0026thinsp;36.407, and Z\u0026thinsp;=\u0026thinsp;17.024, which align with the established binding pocket. These factors facilitated the accurate localization of the docking point. The resultant grid file was the basis for in-silico docking tests performed using AutoDock Vina.\u003c/p\u003e\n\u003ch3\u003eMolecular Docking\u003c/h3\u003e\n\u003cp\u003eMolecular docking was performed via AutoDock Vina, commencing with High-Throughput Virtual Screening (HTVS), which included adjustable ligand sampling to find prospective candidates. The highest-ranked compounds from HTVS underwent Standard Precision (SP) docking using flexible ligand sampling. Thereafter, Extra Precision (XP) docking with fixed ligand sampling (refinement alone) was conducted to enhance ligand-receptor interactions. The final selections for further analysis were determined by docking scores and the quality of binding conformations.\u003c/p\u003e\n\u003ch3\u003eBinding Free Energy Calculation\u003c/h3\u003e\n\u003cp\u003eThe stability of SGCE-ligand complexes was assessed using the Molecular Mechanics Generalized Born Surface Area (MM/GBSA) technique. Calculations were conducted using the Prime MM-GBSA module in Schr\u0026ouml;dinger Maestro 11.5, utilizing the OPLS3 force field and the VSGB solvent model. Default parameters were employed for supplementary configurations. The computations provide quantitative insights into the binding efficiency and stability of the complexes, enabling the selection of the most stable interactions.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePharmacophore Modelling\u003c/h2\u003e \u003cp\u003eThe critical pharmacophore characteristics associated with the interaction between Hypericin and the SGCE active site were determined by developing an e-pharmacophore hypothesis utilizing the Phase module in Schr\u0026ouml;dinger Maestro 11.5. This method facilitated the visualization of important pharmacophoric features, including hydrogen bond donors, acceptors, and hydrophobic areas, which are crucial for the design and assessment of prospective SGCE inhibitors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eADMET Assessment (Pharmacokinetic Analysis)\u003c/h3\u003e\n\u003cp\u003eThe ADMET profiles of Hypericin and the reference SGCE inhibitor were assessed using SwissADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and PROTOX-II (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tox-new.charite.de/protox_II/\u003c/span\u003e\u003cspan address=\"https://tox-new.charite.de/protox_II/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis in silico investigation evaluated essential pharmacokinetic characteristics, including bioavailability, drug-likeness, and toxicity. The findings provide insights into the appropriateness of Hypericin as a prospective pharmacological candidate for SGCE inhibition, facilitating the enhancement of its therapeutic profile.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study found a collection of naturally occurring SGCE inhibitors from the PubChem database, consisting of more than 320 candidate chemicals. Of them, six exhibited substantial binding affinities for the SGCE enzyme. The substances comprised Hypericin, Hypocrellin A2, Stictic Acid, Viola Styrene, and a standard reference ligand from the database. The reference ligand, chosen for its established efficacy, demonstrated a Ligand Efficiency Value (LVS) score of -14.232, which acted as a standard for assessing the binding efficiency of the other ligands, as seen in Figure 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter receptor-ligand interactions, a significant increase in the total polar surface area (TPSA) was noted for the conventional ligand, along with an enhanced Ligand Efficiency Value (LVS) score of -14.03, as shown in Table\u0026nbsp;1. This increased negativity signifies a decreased binding energy need and heightened receptor affinity, suggesting a strong interaction. Moreover, the elevated ligand concentration next to the receptor indicates a strong likelihood of tissue-protein interaction, which may result in a substantial volume of distribution and an extended elimination half-life (t\u0026frac12;).\u003c/p\u003e \u003cp\u003eHypocrellin A2 has emerged as a viable candidate, with a high SlogP value of 4, which indicates moderate water solubility and an ideal equilibrium between hydrophilicity and lipophilicity. These characteristics improve its plasma bioavailability and promote effective renal excretion, perhaps resulting in a reduced elimination half-life (t\u0026frac12;). Upon binding, Hypocrellin A2 exhibited a notable radial plot rise from 546.5 to 646.5, with an LVS score of -12.953, somewhat inferior to the standard ligand by about 2 points. Notwithstanding this little variation, the substantial binding capacity designates Hypocrellin A2 as a compelling subject for more research.\u003c/p\u003e \u003cp\u003eHypericin, a naturally occurring chemical (PubChem ID: 3663), demonstrated unique pharmacokinetic properties. It was characterized by a narrow radial plot, high molecular weight, poor lipophilicity, and increased TPSA, signifying its predominant localization inside the intravascular compartment. This restricted dispersion is associated with a reduced elimination half-life and a decreased volume of distribution. Notwithstanding these limitations, its Ligand Efficiency Value (LVS) score of -14, akin to that of the control ligand, underscores its promise as a viable option for subsequent in vitro and in vivo assessments.\u003c/p\u003e \u003cp\u003eA thorough investigation demonstrated that AG2P1001 and Hypericin had the most advantageous binding interactions and stability, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. AG2P1001 (control ligand) exhibited strong binding affinity with an MM/GBSA score of -38.75. Hypericin demonstrated exceptional stability, achieving an MM/GBSA score of -40.36, so it is established as an optimal choice for in vitro and in vivo investigations. Hypocrellin A2 exhibited significant binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), scoring \u0026minus;\u0026thinsp;37.44. However, it was less effective than AG2P1001 and Hypericin. Conversely, Stictic Acid and Viola Styrene demonstrated diminished interactions, with MM/GBSA values of -32.76 and \u0026minus;\u0026thinsp;34.62, respectively, highlighting their restricted efficacy as inhibitors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;1: Comparative Analysis of Physicochemical Properties, Binding Scores, and MM/GBSA Values of Selected Ligands in Docked and Undocked States.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLigand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlog-P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTPSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLVS Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMM/GBSA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG2P1001 (Undocked)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG2P1001 (Docked)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-14.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-38.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypocrellin A2 (Undocked)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypocrellin A2 (Docked)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-37.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStictic Acid (Undocked)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStictic Acid (Docked)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-32.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViola Styrene (Undocked)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViola Styrene (Docked)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-34.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypericin (Undocked)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypericin (Docked)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-14.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-40.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe toxicity profile of Hypericin suggests the ability to interact with cytochrome P450 (CYP) enzymes, namely CYP1A2, CYP2C9, and CYP2C19, which may affect pharmacokinetics and drug-drug interactions. Moderate interactions with CYP3A4 and CYP2D6 were noted. Hypericin possesses a projected LD50 of 1000 mg/kg (Toxicity Class 4, GHS), indicating little overall toxicity. It demonstrates little nephrotoxicity, mild neurotoxicity, and carcinogenicity hazards, underscoring its potential as a safe and effective therapeutic option for further advancement.\u003c/p\u003e \u003cp\u003eAn extensive investigation of bond interactions was performed to assess the protein-ligand complex further, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The hydrogen bond characteristics, comprising distances (H-A and D-A) and donor angles, were analyzed to evaluate their strength and orientation. Reduced H-A distances, exemplified by the 2.45 \u0026Aring; between LYS and the ligand, signify strong hydrogen bonding, which substantially enhances the stability of the complex. This comprehensive research offers essential insights into the interaction dynamics inside the active site.\u003c/p\u003e \u003cp\u003eEssential residues substantially enhance binding stability. ASN 451 and LYS 471 function as donors, exhibiting robust contacts marked by advantageous angles and short H-A distances of 2.83 \u0026Aring; and 2.45 \u0026Aring;, respectively, as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These factors underscore their crucial function in enhancing binding affinity. In contrast, ASP 458 and GLU 473 function as acceptors, with D-A lengths of roughly 3.9 \u0026Aring;, which suggests moderate hydrogen bonding strength. These findings emphasize the intricate interaction of residues within the binding contact.\u003c/p\u003e \u003cp\u003eThe binding interface study indicates that THR 474 establishes two hydrogen bonds with the ligand but at marginally extended distances, which may introduce flexibility to the binding site. This flexibility may affect the firmness of the entire complex. The reduced distances and advantageous angles of residues like ASN 451 and LYS 471 suggest strong hydrogen bonding, enhancing binding affinity and complex stability. These interactions together highlight a persistent binding location, therefore increasing the ligand's functional efficiency within the protein's active pocket.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey Hydrogen Bond Interactions Between Protein Residues and Hypericin Ligand in the SGC Enzyme Complex.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteracting residues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBond Distance (A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDonor Angle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDonor Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAcceptor Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASN451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASP458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYS471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN3+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHR474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHR474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe iMODS server utilizes Normal Mode Analysis (NMA) to investigate collective movements in macromolecules, facilitating the evaluation of structural flexibility in cellular environments. NMA computes frequencies related to motion magnitudes and deformation vectors that signify atomic displacement directions, which is crucial for assessing molecular adaptability. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA displays the findings of molecular dynamics (MD) simulations and normal mode analysis (NMA) for the Hypericin/sGC enzyme combination. Flexible zones were found by repeatedly altering the structure along low-energy modes and optimizing the root mean square deviation (RMSD). Peaks in deformability graphs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) signify flexible regions such as hinges, whereas lower values represent stiff sections.\u003c/p\u003e \u003cp\u003eThe B-factor, obtained via Normal Mode Analysis (NMA), measures the relative magnitude of atomic displacements in the Hypericin-sGC complex around its equilibrium conformation. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eD illustrates B-factor graphs that correlate the mobility of the complex with Protein Data Bank (PDB) RMSD values. The eigenvalues of normal modes, which signify structural stiffness, are directly associated with the energy necessary for deformation. Reduced eigenvalues indicate more flexibility, with the Hypericin-sGC complex exhibiting a major mode eigenvalue of 1.921427e-04, reflecting minimal energy requirements for deformation and highlighting its structural integrity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThe Elastic Network Model (ENM), seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, illustrates the interatomic connections within the sGC structure, highlighting their significance in preserving structural integrity. Atomic bonds are illustrated as springs, with stiffness levels indicated by colour: darker grey denotes increased rigidity, whilst lighter hues reflect flexibility. This research showed areas of increased stiffness, which contribute to the protein's stability. Both flexible and stiff sections maintained a steady equilibrium throughout the simulation, hence affirming the structural integrity of the sGC enzyme.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eE presents a variance panel that delineates the distribution of variation across modes, with purple bars representing the variance of individual components and green bars indicating cumulative variance, therefore emphasizing each mode's contribution to macromolecular motion. The covariance matrix examines the motions of residue pairs, where correlated movements (red) signify synchronized motion, uncorrelated movements (white) denote independent motion, and anti-correlated movements (blue) imply opposing directional movement. These patterns are crucial for comprehending the dynamic connections and functional links within various areas of the sGC enzyme.\u003c/p\u003e \u003cp\u003eThese findings provide essential insights into the dynamic behavior and stability of the Hypericin-sGC enzyme combination. The strong interactions between Hypericin and the sGC enzyme underscore its potential as a reliable and powerful inhibitor. This stability may significantly contribute to the restoration of cellular homeostasis and the alleviation of pathological processes linked to sepsis and similar diseases, indicating its potential as a therapeutic agent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eSepsis-induced vasoplegia is a critical and potentially fatal consequence of sepsis, principally caused by excessive production of NO. This syndrome results in considerable vasodilation, compromised vascular tone, and multi-organ failure, highlighting the necessity for novel treatment approaches (\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The restricted effectiveness of existing therapies underscores the necessity for focused approaches. This work utilized computational drug design tools to uncover effective inhibitors of sGC enzyme, a crucial enzyme in the nitric oxide-mediated vasodilation pathway. After screening more than 320 natural sGC inhibitors, Hypericin and Hypocrellin A2 were identified as intriguing candidates with significant binding affinities.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComputational Drug Design Methodology\u003c/h2\u003e \u003cp\u003eThe virtual screening technique utilized a thorough methodology encompassing protein and ligand preparation, binding site evaluation, and stratified docking methods, including High-Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP). These approaches precisely forecasted interactions between bioactive substances and sGC. Docking scores and binding energy indicated that lead compounds had better binding affinities than established inhibitors. This strategy, aligned with previous research revealing c-Myc inhibitors, highlights the dependability of computational methods in drug development, further validated by employing several docking approaches (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBinding Free Energy Analysis\u003c/h2\u003e \u003cp\u003eWe conducted Molecular Mechanics with Generalized Born and Surface Area (MMGBSA) binding free energy calculations to confirm docking results for ligand-enzyme complexes. Hypericin and Hypocrellin A2 exhibited significant interactions between sGC and Hypericin, displaying low binding energy and underscoring their therapeutic potential. The MMGBSA approach accurately assesses binding affinities by partitioning free energy into van der Waals, electrostatic, and solvation contributions. This method, averaged across molecular dynamics snapshots, demonstrates Hypericin's strong affinity via hydrogen bonding and hydrophobic interactions, emphasizing the significance of computational techniques in drug discovery (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePharmacokinetic Properties\u003c/h2\u003e \u003cp\u003eHypericin demonstrated favourable pharmacokinetic characteristics, such as significant tissue-binding capacity and a prolonged half-life. The augmented total polar surface area upon receptor engagement improves its stability and dispersion, hence enhancing its therapeutic potential. In contrast, Hypocrellin A2 exhibited advantageous water solubility (logP\u0026thinsp;=\u0026thinsp;4), enhancing bioavailability and facilitating effective renal clearance. These findings correspond with previous research emphasizing the beneficial absorption and distribution properties of natural substances. Furthermore, Hypericin's compliance with Lipinski's rule of five highlights its drug-like properties, providing a significant advantage over traditional inhibitors that frequently violate these standards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eToxicity Assessment\u003c/h2\u003e \u003cp\u003eMetabolic pathways can significantly influence a drug's bioavailability and half-life, leading to differences in therapeutic effectiveness. The metabolic profile of Hypericin, a principal ingredient in St. John's Wort (Hypericum perforatum), highlights its substantial interactions with several cytochrome P450 (CYP) enzymes. It was reported that many CYP enzymes participate in metabolising several drugs (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), considering that CYP enzymes affect Hypericin's pharmacokinetics and possible therapeutic uses (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The active and inactive interaction clusters of Hypericin in this study indicate significant interactions with CYP1A2, CYP2C9, and CYP2C19 (Fig.\u0026nbsp;6), indicating unique metabolic pathways essential for comprehending its therapeutic potential and a considerable probability of biotransformation of Hypericin through these enzymes. The co-administration of Hypericin with pharmaceuticals metabolized by CYP enzymes may modify the plasma concentrations of the concomitant medications, potentially reducing effectiveness or exacerbating side effects. Prior research indicates that St. John's Wort extracts containing Hypericin can suppress the activities of CYP1A2 and CYP2C9, heightening concerns about its pharmacokinetic interactions with other drugs (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, considerable interaction probabilities with CYP3A4 and CYP2D6 indicate probable but clinically significant alterations in drug metabolism. Although Hypericin does not markedly stimulate CYP3A4 activity, its possible effect on the metabolism of CYP3A4 substrates necessitates vigilant monitoring during simultaneous drug administration (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The toxicological assessment of Hypericin indicates a projected LD50 of 1000 mg/kg, categorizing it under Toxicity Class 4 according to the Globally Harmonized System (GHS). This classification, signifying a reduced toxicity profile compared to more dangerous compounds, highlights a comparatively broad therapeutic window While underscoring the importance of careful dosage monitoring to reduce side effects. The low likelihood of neurotoxicity and carcinogenicity further supports Hypericin's safety for therapeutic use. Moreover, the limited toxicological hazards to cardiac and immunological processes enhance its potential in fields like cancer, where safety profiles are paramount. Notwithstanding these encouraging outcomes, ongoing study is essential. Thorough experimental validation is necessary to clarify the mechanisms underlying Hypericin's interactions with CYP enzymes, and additional investigation into the clinical ramifications of these interactions is crucial to guarantee patient safety and therapeutic effectiveness as Hypericin becomes increasingly significant in pharmacological research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the potential of natural chemicals in drug development and the crucial role of computational methods in identifying prospective therapeutic agents. Here, we identify Hypericin as a promising therapeutic agent for treating sepsis-induced vasoplegia and hypotension by inhibiting the sGC enzyme and reducing multi-organ failure. Moreover, Hypericin's natural origin and pharmacological characteristics provide a twofold benefit\u0026mdash;novel therapeutic potential and fewer toxicity concerns than conventional medicines. Therefore, this research enhances the comprehension of sepsis treatment options and underscores the incorporation of computational drug design into modern pharmacology. Nonetheless, in vitro and in vivo validations are essential to verify these computational findings and to evaluate Hypericin's therapeutic potential thoroughly. These contributions substantially enhance current discussions on sepsis therapy, facilitating future advancements in addressing this life-threatening illness. Utilizing natural chemicals such as Hypericin, future pharmaceutical strategies may effectively and safely target complicated conditions like sepsis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe deeply thank Abuzor Mohamed Mohyeldin Khalil for his invaluable guidance and expertise throughout this study. We also sincerely thank Dr Fatema and her colleagues for their meticulous editing and insightful feedback.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declared no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYassir Ali Almofti Ahmed\u003c/p\u003e\n\u003cp\u003eDepartment of Molecular Biology \u0026amp; Bioinformatics,\u0026nbsp;College of Veterinary Medicine,\u0026nbsp;University of Bahri\u003c/p\u003e\n\u003cp\u003ePhone number: +249901589515; Email: [email protected]\u003c/p\u003e\n\u003cp\u003eOrchid ID: 0000-0002-7174-8417\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYousif Ali Ahmed Suleiman\u003c/p\u003e\n\u003cp\u003eAl Adan hospital, Kuwait City, Hadiah Area, Al Ahmade province\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePhone number: +0096597550690; Email: [email protected]\u003c/p\u003e\n\u003cp\u003eOrchid ID: 0009-0003-2606-386X\u003c/p\u003e\n\u003cp\u003eAbuzor Mohamed Mohyeldin Khalil\u003c/p\u003e\n\u003cp\u003eInstitute of Endemic Diseases, University of Khartum, Sudan\u003c/p\u003e\n\u003cp\u003ePhone number: +971501129550; Email: [email protected]\u003c/p\u003e\n\u003cp\u003eOrchid ID:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.A.A.A. is the principal investigator and the main supervisor. Y.A.A.S. formulated the research idea, screened the natural drug from PubChem, defined the control ligand and the structure of the tested receptor, performed all the experiments, evaluated the data and prepared the final manuscript. A.M.M.K. helped in the experimental procedures and data analysis to prepare the main manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Ting Y, Li M, Li Y, Tan Y, Long Y (2024) From immune dysregulation to organ dysfunction: understanding the enigma of Sepsis. Front Microbiol 15:1415274\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R et al (2017) Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. 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Immunol Rev 249(1):158\u0026ndash;175\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu R, Kang Y, Chen L (2021) Activation mechanism of human soluble guanylate cyclase by stimulators and activators. Nat Commun 12(1):5492\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarah C, Michel LY, Balligand J-L (2018) Nitric oxide signalling in cardiovascular health and disease. Nat Reviews Cardiol 15(5):292\u0026ndash;316\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandner P (2018) From molecules to patients: exploring the therapeutic role of soluble guanylate cyclase stimulators. Biol Chem 399(7):679\u0026ndash;690\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCzaikoski PG, Nascimento DBC, Spiller F, Cunha FQ (2009) Heme oxygenase and soluble guanylate cyclase mediate the neutrophil migration failure to the lung in severe sepsis induced by pneumonia. Crit Care 13(3):P24\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandes D, Sordi R, Pacheco LK, Nardi GM, Heckert BT, Villela CG et al (2009) Late, but Not Early, Inhibition of Soluble Guanylate Cyclase Decreases Mortality in a Rat Sepsis Model. J Pharmacol Exp Ther 328(3):991\u0026ndash;999\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCauwels A, Van Molle W, Janssen B, Everaerdt B, Huang P, Fiers W et al (2000) Protection against TNF-Induced Lethal Shock by Soluble Guanylate Cyclase Inhibition Requires Functional Inducible Nitric Oxide Synthase. Immunity 13(2):223\u0026ndash;231\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Gao L, Hu J, Wang C, Hagedoorn P-L, Li N et al (2022) Hypericin: Source, Determination, Separation, and Properties. Sep Purif Reviews 51(1):1\u0026ndash;10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Wei Q, Tian L, Huang Z, Tang Y, Wen Y et al (2024) Advancements and Future Prospects in Hypocrellins Production and Modification for Photodynamic Therapy. Fermentation 10(11):559\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePecchiari M, Pontikis K, Alevrakis E, Vasileiadis I, Kompoti M, Koutsoukou A (2021) Cardiovascular responses during sepsis. Compr Physiol 11(2):1605\u0026ndash;1652\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimmoun A, Ducrocq N, Levy B (2013) Mechanisms of vascular hyporesponsiveness in septic shock. Curr Vasc Pharmacol 11(2):139\u0026ndash;149\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlamami A, Rahhal A, Alqudah B, Shebani A, Alammora A, Mohammad H et al (2024) Clinical Outcomes of Angiotensin II Therapy in Vasoplegic Shock: A Systematic Review and Meta-Analysis. Life 14(9):1085\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgu P, Afiukwa C, Orji O, Ezeh E, Ofoke I, Ogbu C et al (2023) Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Sci Rep 13(1):13398\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ (2016) Computational protein\u0026ndash;ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc 11(5):905\u0026ndash;919\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForouzesh N, Mishra N (2021) An effective MM/GBSA protocol for absolute binding free energy calculations: A case study on SARS-CoV-2 spike protein and the human ACE2 receptor. Molecules 26(8):2383\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanley L (2024) Drug metabolism. Elsevier, Pharmacognosy, pp 597\u0026ndash;624\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKomoroski BJ, Zhang S, Cai H, Hutzler JM, Frye R, Tracy TS et al (2004) Induction and inhibition of cytochromes P450 by the St. John's wort constituent hyperforin in human hepatocyte cultures. Drug Metab Dispos 32(5):512\u0026ndash;518\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObach RS (2000) Inhibition of human cytochrome P450 enzymes by constituents of St. John's Wort, an herbal preparation used in the treatment of depression. J Pharmacol Exp Ther 294(1):88\u0026ndash;95\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenderson L, Yue Q, Bergquist C, Gerden B, Arlett P (2002) St John's wort (Hypericum perforatum): drug interactions and clinical outcomes. Br J Clin Pharmacol 54(4):349\u0026ndash;356\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHakkola J, Hukkanen J, Turpeinen M, Pelkonen O (2020) Inhibition and induction of CYP enzymes in humans: an update. Arch Toxicol 94(11):3671\u0026ndash;3722\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Al adan hospital department of internal medicine ","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":"Nitric Oxide receptor inhibitor, Hypericin, Sepsis, Hypotension, Computational drug design","lastPublishedDoi":"10.21203/rs.3.rs-5678666/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5678666/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSepsis-related hypotension is a life-threatening condition due to systemic infection leading to widespread inflammation and blood vessel dilation. This can cause a dramatic drop in blood pressure, impairing blood flow to vital organs and potentially leading to organ failure and death. NO was recognized as a significant factor in sepsis in 1990 and became an important therapeutic target. NO plays a dual role in sepsis, exhibiting both beneficial and harmful effects. Inhibiting sGC may help reduce the excessive vasodilation associated with sepsis-induced vasoplegia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilized CADD to screen over 320 naturally occurring compounds from the PubChem database for potential sGC inhibitors. A comprehensive virtual screening process, which included protein-ligand docking, binding free energy calculations, and pharmacokinetic profiling, led to the identification of promising candidates such as Hypericin and Hypocrellin A2.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThese compounds demonstrated superior binding affinities and pharmacokinetic properties compared to existing inhibitors. Hypericin achieved a docking score of -14.232, indicating strong interactions with the receptor. It also exhibited favourable pharmacokinetic characteristics, including significant tissue-binding potential and stability within the binding pocket, as well as low predicted toxicity and a substantial safety margin.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis research lays the groundwork for future in vitro and in vivo studies, which could improve Hypericin-based effective therapies for sepsis-induced vasoplegia and hypotension.\u003c/p\u003e","manuscriptTitle":"Endothelial soluble guanylyl cyclase enzyme inhibitors as novel target for treatment of sepsis related hypotension","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-20 07:44:32","doi":"10.21203/rs.3.rs-5678666/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":"b6421d9a-0ce4-4ad0-97f9-ddb77aff97c4","owner":[],"postedDate":"December 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41843511,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2024-12-20T07:44:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-20 07:44:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5678666","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5678666","identity":"rs-5678666","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}