Molecular Docking and Molecular Dynamics Simulation Study of Creatinine Interaction with Human Protein 7XTQ

Molecular Docking and Molecular Dynamics Simulation Study of Creatinine Interaction with Human Protein 7XTQ | 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 Molecular Docking and Molecular Dynamics Simulation Study of Creatinine Interaction with Human Protein 7XTQ Nepolean R, Ramesh K This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8921800/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 Creatinine is an endogenous metabolic by-product widely employed as a clinical biomarker for renal function assessment. Despite its physiological importance, limited information is available regarding its molecular-level interaction behavior with human proteins. In this study, an integrated computational approach combining molecular docking, molecular dynamics (MD) simulations, and in silico ADMET analysis was employed to investigate the interaction profile of creatinine with the human protein structure 7XTQ. Initial blind docking was performed using AutoDock Vina, followed by post-docking MD simulations to evaluate the structural stability and dynamic behavior of the protein-ligand complex. Docking results indicated weak surface-level interactions, which were further assessed through MD-derived parameters including proteins and ligand RMSD, RMSD, radius of gyration, and hydrogen bond analysis. MD simulations demonstrated overall structural stability of the protein and persistent proximity of creatinine at the protein surface without deep pocket insertion. ADMET predictions confirmed the biocompatible and non-toxic nature of creatinine. Collectively, these results suggest that creatinine exhibits weak, non-specific interactions with protein 7XTQ, providing insights into metabolite-protein surface recognition rather than therapeutic inhibition. Creatinine 7XTQ molecular docking molecular dynamics simulation metabolite-protein interaction ADMET Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Creatinine is a low-molecular-weight nitrogenous compound produced from creatinine phosphate metabolism in skeletal muscle and released into systemic circulation at a widely used as a clinical biomarker for kidney function assessment and estimation of glomerular filtration rate [ 1 – 3 ]. As an endogenous metabolite, creatinine circulates continuously within biological systems and may transiently encounter various proteins under physiological conditions. Protein-metabolite interactions, although often weak and non-specific, play an important role in molecular recognition, surface adsorption, and background binding phenomena in biological environments [ 4 , 5 ]. Such interactions are increasingly relevant in metabolomics, systems biology, and structural bioinformatics, where understanding non-specific binding behavior is essential for accurate interpretation of experimental data [ 6 ]. Molecular docking is a widely adapted computational technique for exploring ligand-protein interaction propensities at the atomic level. While docking is most commonly applied in structure-based drug discovery, it has also been used to investigate endogenous metabolites and their interaction tendencies with protein surfaces [ 7 – 9 ]. Blind docking approaches allows unbiased exploration of protein surfaces, particularly when no predefined binding site is available [ 10 ]. However, docking provides a static representation of interactions and does not account for protein flexibility, solvent effects, or time-dependent behaviour. Molecular dynamics (MD) simulations overcome these limitations by modelling atomic motion over time, enabling assessment of structural stability, interaction persistence, and conformational changes [ 11 – 13 ]. The integration of docking and MD simulations is therefore considered a robust strategy for validating weak or transient interactions, particularly for small polar metabolites. In this study, an integrated computational workflow combining molecular docking, molecular dynamics simulations, and in silico ADMET analysis was employed to investigate the interaction behavior of creatinine with the human protein structure 7XTQ. This work does not propose therapeutic or inhibitory activity; instead, it aims to characterize the molecular and dynamic features of potential non-specific metabolite-protein interactions. The overall computational strategy used in this study is summarized in Fig. 1 . Materials and Methods Protein Preparation The three-dimensional structure of human protein 7XTQ was obtained from the Protein Data Bank. Protein preprocessing was performed using pdb2pqr (version 3.0), applying the AMBER force field and adjusting protonation states to physiological pH (7.4) using PROPKA. The prepared structure was converted to PDBQT format using MGL tools with appropriate charge assignment. Basic information of the 7XTQ protein and creatinine (ligand) used in the docking study mentioned in Table 1 . Protein chain lengths, molecular weights, and dominant residues of protein 7XTQ are mentioned in Table 2 . The prepared protein structure of 7XTQ is shown in Fig. 1. Table 1 Basic information of docking protein (7XTQ) and ligand used in the docking study Parameter Description Protein PDB ID 7XTQ Organism Homosapiens Number of chains 5 Ligand name Creatinine Ligand smiles CN1CC(= O)N=C1N Table 2 Summary of protein chain lengths, molecular weights, and dominant amino acids for protein 7XTQ. Chain ID Length (aa) Molecular weight (Da) Dominant residues A 49 5706.5 K, L, G B 340 37373.5 G, L, T N 127 13786.3 G, S, T R 273 29418.5 L, A, V G 58 6366.3 A, K Ligand Preparation Creatinine was selected as the ligand for this interaction study. The ligand structure was generated using RDKit, with explicit hydrogen addition and geometry optimization performed using the MMFF94 force field. The optimized ligand was converted to PDBQT format using OpenBabel. Molecular Docking Blind docking was carried out using AutoDock Vina to allow unrestricted exploration of the protein surface in the absence of a known binding site. The grid encompassed the entire protein structure. Multiple docking poses were generated, and the top-ranked pose based on predicted binding affinity was selected for further analysis. Molecular Dynamics Stimulation Post-docking MD stimulations of the protein-ligand complex were performed to evaluate structural stability and interaction persistence. MD trajectory analysis included: •Protein RMSD •Ligand RMSD •Root Mean Square Fluctuation (RMSF) •Radius of gyration (Rg) •Hydrogen bond analysis These parameters were used to assess conformational stability, flexibility, compactness, and interaction dynamics throughout the simulation period. ADMET Prediction In silico ADMET analysis was conducted to evaluate absorption, distribution, metabolism, excretion, and toxicity properties of creatinine. The analysis focused on biological compatibility rather than drug-likeness. Results Docking Analysis Docking results revealed weak surface-level interactions between creatinine protein 7XTQ, primarily involving residues ALA60 and THR102 of chain B. Interaction distances were approximately 5–6 Å, indicating non-specific proximity rather than deep pocket binding. Molecular Docking Analysis Figure 3 illustrates the 2D interaction diagram of creatinine with the protein 7XTQ complex obtained from molecular docking analysis. Table 3 summarizes the best binding affinity score and the key residues involved in the creatinine-7XTQ obtained from molecular docking analysis. Table 3 Best Binding Affinity and Key Interacting Residues of Creatinine with Protein 7XTQ Protein 7XTQ Ligand Creatinine Docking Pose Best pose (Rank 10 Binding Affinity (kcal/mol) -4.61 Protein Chain B Interacting Residues ALA60, THR102 Interaction Distance (Å) -5.6 Amino Acid Composition Heatmap of Protein Chains: This heatmap shows the percentage of each amino acid in different protein chains in Fig. 4 . Protein and Ligand Stability (MD Analysis) Protein RMSD analysis showed stabilization after an initial equilibration phase, indicating overall structural stability of the 7XTQ protein during simulation. Ligand RMSD values remain low, suggesting that creatinine maintained consistent proximity to the protein surface without dissociation. Table 4 summarizes key molecular dynamics stability parameters, including RMSD and radius of gyration, indicating that the protein structure remained stable while the ligand exhibited flexible but persistent association with the protein. Table 4 Summary of molecular dynamic stability parameters for the 7XTQ-creatinine complex Parameter Observed Range Interpretation Protein RMSD (Å) ~ 0.8–1.3 Å Protein backbone remained structurally stable throughout the simulation, indicating good equilibration Ligand RMSD (Å) Large fluctuations observed Ligand exhibits high mobility during simulation, suggesting flexible or transient binding behaviour Protein Radius of Gyration (Å) ~ 33.5–34.2 Å Protein maintained compact and folded conformation Ligand Radius of Gyration (Å) ~ 119.45–119.56 Å Ligand conformation remained structurally consistent Ligand-Protein COM Distance (Å) ~ 0.30–0.40 Å Ligand remained in close proximity to the protein throughout the simulation Molecular Dynamics Simulation Analysis RMSD analysis RMSD analysis was performed to evaluate the structural stability of the protein backbone and the bound ligand during the MD simulation. As shown in Fig. 5 , the protein backbone RMSD rapidly increased during the initial equilibration phase and subsequently stabilized around ~ 1–2 Å. Indicating convergence and overall structural stability of Protein 7XTQ. The ligand RMSD relative to the protein binding site remained stable after initial fluctuations, suggesting that creatinine retained its binding pose throughout the simulation without dissociation. Residue Flexibility RMSF analysis revealed moderate fluctuations in loop regions, while residues involved in creatinine interaction exhibited limited flexibility. This suggests that ligand proximity did not induce major conformational disturbances in the protein structure. As shown in Fig. 6 , most residues exhibited low RMSF values, indicating overall structural rigidity of the protein. Higher fluctuations were observed primarily in loop and terminal regions, which is typical for solvent-exposed segments, while residues forming the core secondary structure remained relatively stable. Radius of Gyration The radius of gyration remained stable throughout the simulation, indicating preserved protein compactness and absence of large-scale unfolding events upon ligand association. As shown in Fig. 7 , the Radius of gyration values remained relatively constant throughout the simulation, indicating that the protein maintained its overall folded and compact conformation without major unfolding events. Hydrogen Bond Analysis Hydrogen bond analysis indicated intermittent hydrogen bonding between creatinine and surrounding residues, supporting transient and non-persistent interaction behaviour rather than stable complex formation. ADMET Properties ADMET predictions confirmed that creatinine is highly soluble, non-toxic, and devoid of mutagenic or carcinogenic liabilities, consistent with its endogenous physiological role. In silico ADMET profiling of creatinine was performed to evaluate its drug-likeness and safety characteristics ( Table 5 ). Creatinine exhibited full compliance with Lipinski’s rule of five, high aqueous solubility, good predicted gastrointestinal absorption, and low CYP inhibition liability. Toxicity predictions indicated non-mutagenic behaviour, although a potential hERG liability was observed, warranting cautious interpretation. Table 5 In silico ADMET and drug-likeness profile of creatinine Parameter Predicted value Interpretation/ relevance Molecular weight (Da) 113.12 Very small molecule; favours diffusion LogP (MolLogP) -1.23 Highly hydrophilic Topological polar surface area (Å 2 ) 58.69 Within acceptable range for absorption Hydrogen bond donors 1 Supports H-bond absorption Hydrogen bond acceptors 3 Compatible with binding-site interactions Rotatable bonds 0 Rigid structure; low entropic penalty ESOL LogS 0.23 High aqueous solubility Synthetic accessibility score 3.15 Easy to synthesize Quantitative estimate of drug-likeness (QED) 0.43 Moderate to drug-likeness Lipinski rule violations 0 Fully complaint GI absorption Good Favourable oral absorption BBB permeability Low Limited CNS penetration P-gp substrate Unlikely Reduced efflux risk Caco-2 permeability Medium Moderate intestinal permeability Estimated LD50 5.73 Low acute toxicity (model- based) Discussion The combined docking and molecular dynamics simulation simulations generate that creatinine exhibits weak and non specific interaction behaviour withprotein 7XTQ. Docking analysis suggested surface-level assosciation involving solvent exposed residues, which is consistent with the physiochemical properties of creatinine, including its small size, high polarity, and limited hydrophobic surface area [ 14 , 15 ]. Molecular dynamics simulations further supported these findings by demonstrating overall structural stability of the protein and persistent, yet transient, proximity of creatinine during the simulation period. Similar observations have been reported for endogenous metabolites, which often display low-affinity interactions driven by electrostatic complimentarity and surface accessibility rather than deep pocket binding [ 4 , 6 , 16 ]. RMSD and radius of gyration analyses indicated that creatinine binding did not induce major conformational changes in the protein structure, suggesting the absence of strong allosteric or inhibitory effects. RMSF analysis revealed moderate flexibility in loop regions, a common feature observed in MD simulations of protein-metabolite systems [ 11 , 17 ]. Hydrogen bond analysis showed intermittent hydrogen bond formation, further reinforcing the transient nature of the interaction. Previous studies have emphazised that docking scores and interaction geometries must be interpreted cautiously when dealing with small polar molecules, as static docking may overestimate binding relevance [ 8 , 18 ]. Overall, the integration of docking and MD simulations provided a more realistic interpretation of creatinine-7XTQ interactions, highlighting the importance of dynamic validation when investigating endogenous metabolites. This approach aligns with current best practices in computational structural biology [ 12 , 13 ]. Limitations This study is limited by the absence of experimental validation and free energy calculations such as MM-PBSA. Additionally, the biological relevance of creatinine-7XTQ interactions remains hypothetical and requires further investigation. Conclusion This computational study provides a comprehensive docking and molecular dynamics analysis of creatinine interaction with human protein 7XTQ. The findings indicate weak, non-specific surface interactions that remain dynamically stable without forming a well-defined binding complex. These results contribute to a broader understanding of metabolite-protein interaction behavior and underscore the importance of MD simulations in interpreting docking outcomes for endogenous compounds. Declarations Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Author Contributions Nepolean R conceptualized and designed the study, performed the molecular docking and molecular dynamics simulations, analyzed and interpreted the data, and drafted the original manuscript. Ramesh K interpretation of results, manuscript editing, and critical revision of the intellectual content and supervised the research work, provided methodological guidance, and reviewed the final version of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank the management of Thanthai Roever College of Pharmacy, Perambalur, Tamil Nadu, for carrying out this research work. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethics Approval Ethics approval was not required for this study, as it is based entirely on in silico molecular docking and molecular dynamics simulation methods and did not involve human participants or animals. Consent to Participate Consent to participate is not applicable. Consent to Publish Consent to publish is not applicable. References Perrone RD, Madias NE, Levey AS. Serum creatinine as an index of renal function: new insights into old concepts. Clin Chem. 1992;38(10):1933–1953. doi:10.1093/clinchem/38.10.1933 Stevens LA, Levey AS. Measurement of kidney function. Med Clin North Am. 2005;89(3):457–473. doi:10.1016/j.mcna.2004.11.009 Delanghe JR, Speeckaert MM. Preanalytics in urinalysis. Clin Biochem. 2016;49(18):1346–1350. doi:10.1016/j.clinbiochem.2016.10.016 Nooren IMA, Thornton JM. Diversity of protein–protein interactions. EMBO J. 2003;22(14):3486–3492. doi:10.1093/emboj/cdg359 Kell DB, Oliver SG. How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion. Front Pharmacol. 2014;5:231. doi:10.3389/fphar.2014.00231 Wishart DS. Metabolomics: applications to food science and nutrition research. Trends Food Sci Technol. 2008;19(9):482–493. doi:10.1016/j.tifs.2008.03.003 Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7(2):146–157. doi:10.2174/157340911795677602 Sousa SF, Fernandes PA, Ramos MJ. Protein–ligand docking: current status and future challenges. Proteins. 2006;65(1):15–26. doi:10.1002/prot.21082 Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, et al. A critical assessment of docking programs and scoring functions. J Med Chem. 2006;49(20):5912–5931. doi:10.1021/jm050362n Hetényi C, van der Spoel D. Blind docking of drug-sized compounds to proteins with up to a thousand residues. FEBS Lett. 2006;580(5):1447–1450. doi:10.1016/j.febslet.2006.01.074 Karplus M, McCammon JA. Molecular dynamics simulations of biomolecules. Nat Struct Biol. 2002 Sep;9(9):646-52. doi: 10.1038/nsb0902-646. Erratum in: Nat Struct Biol 2002 Oct;9(10):788. PMID: 12198485. Hollingsworth SA, Dror RO. Molecular Dynamics Simulation for All. Neuron. 2018 Sep 19;99(6):1129-1143. doi: 10.1016/j.neuron.2018.08.011. PMID: 30236283; PMCID: PMC6209097. Gelpí JL, Hospital A, Goñi R, Orozco M. Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem. 2015;10:37–47. doi:10.2147/AABC.S70333 Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol. 2004;1(4):337–341. doi:10.1016/j.ddtec.2004.11.007 Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45(12):2615–2623. doi:10.1021/jm020017n Peters A, Palay SL. The morphology of synapses. J Neurocytol. 1996 Dec;25(12):687-700. doi: 10.1007/BF02284835. PMID: 9023718. Bakan A, Nevins N, Lakdawala AS, Bahar I. Druggability assessment of allosteric proteins by dynamics simulations in the presence of probe molecules. J Chem Theory Comput. 2012;8(7):2435–2447. doi:10.1021/ct300117j Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015 Jul 22;20(7):13384-421. doi: 10.3390/molecules200713384. PMID: 26205061; PMCID: PMC6332083. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8921800","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616409013,"identity":"628bcfd6-14b0-4e1f-aa4f-fa7f6d5826cd","order_by":0,"name":"Nepolean R","email":"","orcid":"","institution":"Thanthai Roever College of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Nepolean","middleName":"","lastName":"R","suffix":""},{"id":616409014,"identity":"344eda50-1b93-4adb-9dd2-ad4fc88c4a3b","order_by":1,"name":"Ramesh 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02:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8921800/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8921800/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106085540,"identity":"90001441-8d9f-4b66-b01b-2c46c78b14a4","added_by":"auto","created_at":"2026-04-03 09:28:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":327017,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8921800/v1/5d99efc98e6fb93218acab7e.png"},{"id":106095125,"identity":"1e0b05ad-a74a-465d-9b83-3443658cb42b","added_by":"auto","created_at":"2026-04-03 11:44:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrepared protein of 7XTQ from GPBAR (G-protein-coupled bile acid receptor)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8921800/v1/5c400dbae2de9e9cd5ca80da.png"},{"id":106085589,"identity":"11dfa3dd-eabe-4f81-b05a-c36cbd6e640d","added_by":"auto","created_at":"2026-04-03 09:28:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2D interaction diagram of creatinine with 7XTQ protein\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8921800/v1/95291e88244a15a73449a59e.png"},{"id":106085533,"identity":"8e5362ea-9abf-4dbc-8b65-9ab85679bcd3","added_by":"auto","created_at":"2026-04-03 09:27:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62783,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap showing amino acid composition percentages across individual chains of protein 7XTQ, highlighting compositional biases relevant to ligand binding.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8921800/v1/6dc61e041d4862e841c71d6c.png"},{"id":106094611,"identity":"8ae7a8dd-c499-4845-988b-5d8e152bf96e","added_by":"auto","created_at":"2026-04-03 11:42:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24379,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRMSD profile of protein backbone and ligand during MD simulation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8921800/v1/ddd1e9df0875ca7edcbb9689.png"},{"id":106085543,"identity":"ab2777bb-24ed-400c-98bb-e729583c7f31","added_by":"auto","created_at":"2026-04-03 09:28:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":100606,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRoot mean square fluctuation (RMSF) plot showing resisdue-wise flexibility of protein 7XTQ during the molecular dynamics simulation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8921800/v1/24b2e109a522e010d4ad5db0.png"},{"id":106094250,"identity":"206c3247-eb03-4cd6-be75-c1cfb7be56af","added_by":"auto","created_at":"2026-04-03 11:41:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":28434,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRadius of gyration of protein 7XTQ during MD simulation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8921800/v1/f435e583631ecd6b0ac4899b.png"},{"id":106724355,"identity":"245fdc78-28c7-486c-8cee-473eaf32b90f","added_by":"auto","created_at":"2026-04-12 18:27:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1643123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8921800/v1/99b44f0e-3175-4f27-bb2e-29c3d299b4b5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular Docking and Molecular Dynamics Simulation Study of Creatinine Interaction with Human Protein 7XTQ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCreatinine is a low-molecular-weight nitrogenous compound produced from creatinine phosphate metabolism in skeletal muscle and released into systemic circulation at a widely used as a clinical biomarker for kidney function assessment and estimation of glomerular filtration rate [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. As an endogenous metabolite, creatinine circulates continuously within biological systems and may transiently encounter various proteins under physiological conditions.\u003c/p\u003e\n\u003cp\u003eProtein-metabolite interactions, although often weak and non-specific, play an important role in molecular recognition, surface adsorption, and background binding phenomena in biological environments [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. Such interactions are increasingly relevant in metabolomics, systems biology, and structural bioinformatics, where understanding non-specific binding behavior is essential for accurate interpretation of experimental data [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eMolecular docking is a widely adapted computational technique for exploring ligand-protein interaction propensities at the atomic level. While docking is most commonly applied in structure-based drug discovery, it has also been used to investigate endogenous metabolites and their interaction tendencies with protein surfaces [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Blind docking approaches allows unbiased exploration of protein surfaces, particularly when no predefined binding site is available [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eHowever, docking provides a static representation of interactions and does not account for protein flexibility, solvent effects, or time-dependent behaviour. Molecular dynamics (MD) simulations overcome these limitations by modelling atomic motion over time, enabling assessment of structural stability, interaction persistence, and conformational changes [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. The integration of docking and MD simulations is therefore considered a robust strategy for validating weak or transient interactions, particularly for small polar metabolites.\u003c/p\u003e\n\u003cp\u003eIn this study, an integrated computational workflow combining molecular docking, molecular dynamics simulations, and in silico ADMET analysis was employed to investigate the interaction behavior of creatinine with the human protein structure 7XTQ. This work does not propose therapeutic or inhibitory activity; instead, it aims to characterize the molecular and dynamic features of potential non-specific metabolite-protein interactions. The overall computational strategy used in this study is summarized in \u003cstrong\u003eFig.\u0026nbsp;1\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch3\u003eProtein Preparation\u003c/h3\u003e\u003cp\u003eThe three-dimensional structure of human protein 7XTQ was obtained from the Protein Data Bank. Protein preprocessing was performed using pdb2pqr (version 3.0), applying the AMBER force field and adjusting protonation states to physiological pH (7.4) using PROPKA. The prepared structure was converted to PDBQT format using MGL tools with appropriate charge assignment. Basic information of the 7XTQ protein and creatinine (ligand) used in the docking study mentioned in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Protein chain lengths, molecular weights, and dominant residues of protein 7XTQ are mentioned in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The prepared protein structure of 7XTQ is shown in \u003cb\u003eFig.\u0026nbsp;1.\u003c/b\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic information of docking protein (7XTQ) and ligand used in the docking study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eProtein PDB ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7XTQ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eOrganism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHomosapiens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNumber of chains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLigand name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLigand smiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCN1CC(= O)N=C1N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of protein chain lengths, molecular weights, and dominant amino acids for protein 7XTQ.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eChain ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eLength (aa)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eMolecular weight (Da)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eDominant residues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e5706.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eK, L, G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e37373.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eG, L, T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e13786.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eG, S, T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e29418.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eL, A, V\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6366.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eA, K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003ch2\u003eLigand Preparation\u003c/h2\u003e\u003cp\u003eCreatinine was selected as the ligand for this interaction study. The ligand structure was generated using RDKit, with explicit hydrogen addition and geometry optimization performed using the MMFF94 force field. The optimized ligand was converted to PDBQT format using OpenBabel.\u003c/p\u003e\u003ch3\u003eMolecular Docking\u003c/h3\u003e\u003cp\u003eBlind docking was carried out using AutoDock Vina to allow unrestricted exploration of the protein surface in the absence of a known binding site. The grid encompassed the entire protein structure. Multiple docking poses were generated, and the top-ranked pose based on predicted binding affinity was selected for further analysis.\u003c/p\u003e\u003ch3\u003eMolecular Dynamics Stimulation\u003c/h3\u003e\u003cp\u003ePost-docking MD stimulations of the protein-ligand complex were performed to evaluate structural stability and interaction persistence. MD trajectory analysis included:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e•Protein RMSD\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e•Ligand RMSD\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e•Root Mean Square Fluctuation (RMSF)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e•Radius of gyration (Rg)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e•Hydrogen bond analysis\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eThese parameters were used to assess conformational stability, flexibility, compactness, and interaction dynamics throughout the simulation period.\u003c/p\u003e\u003ch3\u003eADMET Prediction\u003c/h3\u003e\u003cp\u003eIn silico ADMET analysis was conducted to evaluate absorption, distribution, metabolism, excretion, and toxicity properties of creatinine. The analysis focused on biological compatibility rather than drug-likeness.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDocking Analysis\u003c/h2\u003e \u003cp\u003eDocking results revealed weak surface-level interactions between creatinine protein 7XTQ, primarily involving residues ALA60 and THR102 of chain B. Interaction distances were approximately 5\u0026ndash;6 \u0026Aring;, indicating non-specific proximity rather than deep pocket binding.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular Docking Analysis\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the 2D interaction diagram of creatinine with the protein 7XTQ complex obtained from molecular docking analysis. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the best binding affinity score and the key residues involved in the creatinine-7XTQ obtained from molecular docking analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBest Binding Affinity and Key Interacting Residues of Creatinine with Protein 7XTQ\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7XTQ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLigand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDocking Pose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBest pose (Rank 10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBinding Affinity (kcal/mol)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein Chain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInteracting Residues\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALA60, THR102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInteraction Distance\u003c/b\u003e (\u0026Aring;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eAmino Acid Composition Heatmap of Protein Chains:\u003c/h3\u003e\n\u003cp\u003eThis heatmap shows the percentage of each amino acid in different protein chains in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProtein and Ligand Stability (MD Analysis)\u003c/h2\u003e \u003cp\u003eProtein RMSD analysis showed stabilization after an initial equilibration phase, indicating overall structural stability of the 7XTQ protein during simulation. Ligand RMSD values remain low, suggesting that creatinine maintained consistent proximity to the protein surface without dissociation. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes key molecular dynamics stability parameters, including RMSD and radius of gyration, indicating that the protein structure remained stable while the ligand exhibited flexible but persistent association with the protein.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of molecular dynamic stability parameters for the 7XTQ-creatinine complex\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein RMSD (\u0026Aring;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.8\u0026ndash;1.3 \u0026Aring;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein backbone remained structurally stable throughout the simulation, indicating good equilibration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLigand RMSD (\u0026Aring;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge fluctuations observed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLigand exhibits high mobility during simulation, suggesting flexible or transient binding behaviour\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein Radius of Gyration (\u0026Aring;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;33.5\u0026ndash;34.2 \u0026Aring;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein maintained compact and folded conformation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLigand Radius of Gyration (\u0026Aring;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;119.45\u0026ndash;119.56 \u0026Aring;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLigand conformation remained structurally consistent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLigand-Protein COM Distance (\u0026Aring;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.30\u0026ndash;0.40 \u0026Aring;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLigand remained in close proximity to the protein throughout the simulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Dynamics Simulation Analysis\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eRMSD analysis\u003c/h2\u003e \u003cp\u003eRMSD analysis was performed to evaluate the structural stability of the protein backbone and the bound ligand during the MD simulation. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the protein backbone RMSD rapidly increased during the initial equilibration phase and subsequently stabilized around ~\u0026thinsp;1\u0026ndash;2 \u0026Aring;. Indicating convergence and overall structural stability of Protein 7XTQ. The ligand RMSD relative to the protein binding site remained stable after initial fluctuations, suggesting that creatinine retained its binding pose throughout the simulation without dissociation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eResidue Flexibility\u003c/h2\u003e \u003cp\u003eRMSF analysis revealed moderate fluctuations in loop regions, while residues involved in creatinine interaction exhibited limited flexibility. This suggests that ligand proximity did not induce major conformational disturbances in the protein structure. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, most residues exhibited low RMSF values, indicating overall structural rigidity of the protein. Higher fluctuations were observed primarily in loop and terminal regions, which is typical for solvent-exposed segments, while residues forming the core secondary structure remained relatively stable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRadius of Gyration\u003c/h2\u003e \u003cp\u003eThe radius of gyration remained stable throughout the simulation, indicating preserved protein compactness and absence of large-scale unfolding events upon ligand association. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the Radius of gyration values remained relatively constant throughout the simulation, indicating that the protein maintained its overall folded and compact conformation without major unfolding events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eHydrogen Bond Analysis\u003c/h2\u003e \u003cp\u003eHydrogen bond analysis indicated intermittent hydrogen bonding between creatinine and surrounding residues, supporting transient and non-persistent interaction behaviour rather than stable complex formation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eADMET Properties\u003c/h2\u003e \u003cp\u003eADMET predictions confirmed that creatinine is highly soluble, non-toxic, and devoid of mutagenic or carcinogenic liabilities, consistent with its endogenous physiological role. In silico ADMET profiling of creatinine was performed to evaluate its drug-likeness and safety characteristics \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Creatinine exhibited full compliance with Lipinski\u0026rsquo;s rule of five, high aqueous solubility, good predicted gastrointestinal absorption, and low CYP inhibition liability. Toxicity predictions indicated non-mutagenic behaviour, although a potential hERG liability was observed, warranting cautious interpretation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIn silico ADMET and drug-likeness profile of creatinine\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredicted value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation/ relevance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular weight (Da)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery small molecule; favours diffusion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogP (MolLogP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighly hydrophilic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopological polar surface area (\u0026Aring;\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithin acceptable range for absorption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydrogen bond donors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupports H-bond absorption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydrogen bond acceptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompatible with binding-site interactions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotatable bonds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRigid structure; low entropic penalty\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESOL LogS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh aqueous solubility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSynthetic accessibility score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEasy to synthesize\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantitative estimate of drug-likeness (QED)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate to drug-likeness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipinski rule violations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFully complaint\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFavourable oral absorption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBBB permeability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLimited CNS penetration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-gp substrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnlikely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduced efflux risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaco-2 permeability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate intestinal permeability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated LD50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow acute toxicity (model- based)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe combined docking and molecular dynamics simulation simulations generate that creatinine exhibits weak and non specific interaction behaviour withprotein 7XTQ. Docking analysis suggested surface-level assosciation involving solvent exposed residues, which is consistent with the physiochemical properties of creatinine, including its small size, high polarity, and limited hydrophobic surface area [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMolecular dynamics simulations further supported these findings by demonstrating overall structural stability of the protein and persistent, yet transient, proximity of creatinine during the simulation period. Similar observations have been reported for endogenous metabolites, which often display low-affinity interactions driven by electrostatic complimentarity and surface accessibility rather than deep pocket binding [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRMSD and radius of gyration analyses indicated that creatinine binding did not induce major conformational changes in the protein structure, suggesting the absence of strong allosteric or inhibitory effects. RMSF analysis revealed moderate flexibility in loop regions, a common feature observed in MD simulations of protein-metabolite systems [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHydrogen bond analysis showed intermittent hydrogen bond formation, further reinforcing the transient nature of the interaction. Previous studies have emphazised that docking scores and interaction geometries must be interpreted cautiously when dealing with small polar molecules, as static docking may overestimate binding relevance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, the integration of docking and MD simulations provided a more realistic interpretation of creatinine-7XTQ interactions, highlighting the importance of dynamic validation when investigating endogenous metabolites. This approach aligns with current best practices in computational structural biology [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study is limited by the absence of experimental validation and free energy calculations such as MM-PBSA. Additionally, the biological relevance of creatinine-7XTQ interactions remains hypothetical and requires further investigation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis computational study provides a comprehensive docking and molecular dynamics analysis of creatinine interaction with human protein 7XTQ. The findings indicate weak, non-specific surface interactions that remain dynamically stable without forming a well-defined binding complex. These results contribute to a broader understanding of metabolite-protein interaction behavior and underscore the importance of MD simulations in interpreting docking outcomes for endogenous compounds.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eNepolean R\u003c/strong\u003e conceptualized and designed the study, performed the molecular docking and molecular dynamics simulations, analyzed and interpreted the data, and drafted the original manuscript.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRamesh K\u0026nbsp;\u003c/strong\u003einterpretation of results, manuscript editing, and critical revision of the intellectual content and supervised the research work, provided methodological guidance, and reviewed the final version of the manuscript. All authors read and approved the final manuscript.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the management of Thanthai Roever College of Pharmacy, Perambalur, Tamil Nadu, for carrying out this research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was not required for this study, as it is based entirely on in silico molecular docking and molecular dynamics simulation methods and did not involve human participants or animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to participate is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to publish is not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePerrone RD, Madias NE, Levey AS. Serum creatinine as an index of renal function: new insights into old concepts. Clin Chem. 1992;38(10):1933\u0026ndash;1953. doi:10.1093/clinchem/38.10.1933\u003c/li\u003e\n \u003cli\u003eStevens LA, Levey AS. Measurement of kidney function. Med Clin North Am. 2005;89(3):457\u0026ndash;473. doi:10.1016/j.mcna.2004.11.009\u003c/li\u003e\n \u003cli\u003eDelanghe JR, Speeckaert MM. Preanalytics in urinalysis. Clin Biochem. 2016;49(18):1346\u0026ndash;1350. doi:10.1016/j.clinbiochem.2016.10.016\u003c/li\u003e\n \u003cli\u003eNooren IMA, Thornton JM. Diversity of protein\u0026ndash;protein interactions. EMBO J. 2003;22(14):3486\u0026ndash;3492. doi:10.1093/emboj/cdg359\u003c/li\u003e\n \u003cli\u003eKell DB, Oliver SG. How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion. Front Pharmacol. 2014;5:231. doi:10.3389/fphar.2014.00231\u003c/li\u003e\n \u003cli\u003eWishart DS. Metabolomics: applications to food science and nutrition research. Trends Food Sci Technol. 2008;19(9):482\u0026ndash;493. doi:10.1016/j.tifs.2008.03.003\u003c/li\u003e\n \u003cli\u003eMeng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7(2):146\u0026ndash;157. doi:10.2174/157340911795677602\u003c/li\u003e\n \u003cli\u003eSousa SF, Fernandes PA, Ramos MJ. Protein\u0026ndash;ligand docking: current status and future challenges. Proteins. 2006;65(1):15\u0026ndash;26. doi:10.1002/prot.21082\u003c/li\u003e\n \u003cli\u003eWarren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, et al. A critical assessment of docking programs and scoring functions. J Med Chem. 2006;49(20):5912\u0026ndash;5931. doi:10.1021/jm050362n\u003c/li\u003e\n \u003cli\u003eHet\u0026eacute;nyi C, van der Spoel D. Blind docking of drug-sized compounds to proteins with up to a thousand residues. FEBS Lett. 2006;580(5):1447\u0026ndash;1450. doi:10.1016/j.febslet.2006.01.074\u003c/li\u003e\n \u003cli\u003eKarplus M, McCammon JA. Molecular dynamics simulations of biomolecules. Nat Struct Biol. 2002 Sep;9(9):646-52. doi: 10.1038/nsb0902-646. Erratum in: Nat Struct Biol 2002 Oct;9(10):788. PMID: 12198485.\u003c/li\u003e\n \u003cli\u003eHollingsworth SA, Dror RO. Molecular Dynamics Simulation for All. Neuron. 2018 Sep 19;99(6):1129-1143. doi: 10.1016/j.neuron.2018.08.011. PMID: 30236283; PMCID: PMC6209097.\u003c/li\u003e\n \u003cli\u003eGelp\u0026iacute; JL, Hospital A, Go\u0026ntilde;i R, Orozco M. Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem. 2015;10:37\u0026ndash;47. doi:10.2147/AABC.S70333\u003c/li\u003e\n \u003cli\u003eLipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol. 2004;1(4):337\u0026ndash;341. doi:10.1016/j.ddtec.2004.11.007\u003c/li\u003e\n \u003cli\u003eVeber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45(12):2615\u0026ndash;2623. doi:10.1021/jm020017n\u003c/li\u003e\n \u003cli\u003ePeters A, Palay SL. The morphology of synapses. J Neurocytol. 1996 Dec;25(12):687-700. doi: 10.1007/BF02284835. PMID: 9023718.\u003c/li\u003e\n \u003cli\u003eBakan A, Nevins N, Lakdawala AS, Bahar I. Druggability assessment of allosteric proteins by dynamics simulations in the presence of probe molecules. J Chem Theory Comput. 2012;8(7):2435\u0026ndash;2447. doi:10.1021/ct300117j\u003c/li\u003e\n \u003cli\u003eFerreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015 Jul 22;20(7):13384-421. doi: 10.3390/molecules200713384. PMID: 26205061; PMCID: PMC6332083.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Creatinine, 7XTQ, molecular docking, molecular dynamics simulation, metabolite-protein interaction, ADMET","lastPublishedDoi":"10.21203/rs.3.rs-8921800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8921800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCreatinine is an endogenous metabolic by-product widely employed as a clinical biomarker for renal function assessment. Despite its physiological importance, limited information is available regarding its molecular-level interaction behavior with human proteins. In this study, an integrated computational approach combining molecular docking, molecular dynamics (MD) simulations, and in silico ADMET analysis was employed to investigate the interaction profile of creatinine with the human protein structure 7XTQ. Initial blind docking was performed using AutoDock Vina, followed by post-docking MD simulations to evaluate the structural stability and dynamic behavior of the protein-ligand complex. Docking results indicated weak surface-level interactions, which were further assessed through MD-derived parameters including proteins and ligand RMSD, RMSD, radius of gyration, and hydrogen bond analysis. MD simulations demonstrated overall structural stability of the protein and persistent proximity of creatinine at the protein surface without deep pocket insertion. ADMET predictions confirmed the biocompatible and non-toxic nature of creatinine. Collectively, these results suggest that creatinine exhibits weak, non-specific interactions with protein 7XTQ, providing insights into metabolite-protein surface recognition rather than therapeutic inhibition.\u003c/p\u003e","manuscriptTitle":"Molecular Docking and Molecular Dynamics Simulation Study of Creatinine Interaction with Human Protein 7XTQ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 09:27:30","doi":"10.21203/rs.3.rs-8921800/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":"e1980319-846f-4389-b936-8eae6a873977","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T09:27:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 09:27:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8921800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8921800","identity":"rs-8921800","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}