In Silico Analysis Determining the Binding Interactions of NAD(P)H: Quinone Oxidoreductase 1 and Resveratrol via Docking and Molecular Dynamic Simulations
Year 2023,
, 280 - 288, 21.12.2023
Santosh Kumar Behera
,
Christoffer Lambring
,
Albina Hashmi
,
Sriharika Gottipolu
,
Riyaz Basha
Abstract
Objective: NAD(P)H: Quinone oxidoreductase1 (NQO1) plays a crucial role in cellular defense against oxidative stress. Overexpression of NQO1 is linked to various cancer pathways. Despite its potential, the actual mechanisms to inhibit NQO1 and increase the efficacy of standard therapeutic options are not yet established. Resveratrol is an anti-cancer polyphenol found in dietary products and red wine. The objective of this investigation is to employ in silico methods to explore how resveratrol interacts with NQO1.
Materials and Methods: Docking analysis of resveratrol against NQO1 was performed using Glide. The most efficiently docked complex was characterized and analyzed by measuring intermolecular (IM) hydrogen (H)-bonds and binding energy values, additional hydrophobic, and electrostatic interactions. IM interaction between complexed protein and compound was demonstrated using LigPlot+ and the Schrödinger ligand interaction module. Molecular dynamics tools were employed to examine the physical movement of molecules to evaluate how macromolecular structures relate to their functions.
Results: The results of this investigation depicted a strong affinity of resveratrol againstNQO1followed byMDsimulations (NQO1- resveratrol complex-binding energy: -2.847kcal/mol). Resveratrol’s robust binding affinity through docking and molecular dynamic simulations highlights a significant change around 90 ns. The H-bonds number was inversely linked with the resveratrol-NQO1 complex stability. The NQO1-Resveratrol complex displayed dynamic motion, as revealed by porcupine projections, indicating alterations in its movement and flexibility.
Conclusion: The present in silico analysis suggests a possible alteration in resveratrol’s orientation in the protein binding pocket. The findings encourage further investigation, including validation using in vitro and in vivo assays.
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Year 2023,
, 280 - 288, 21.12.2023
Santosh Kumar Behera
,
Christoffer Lambring
,
Albina Hashmi
,
Sriharika Gottipolu
,
Riyaz Basha
References
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- Preethi S, Arthiga K, Patil AB, Spandana A, Jain V. Review on NAD(P)H dehydrogenase quinone 1 (NQO1) pathway. Mol Biol Rep. 2022;49(9):8907-8924 google scholar
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- Wright JS, Johnson ER, DiLabio GA. Predicting the activity of phenolic antioxidants: Theoretical method, analysis of substituent effects, and application to major families of antioxidants. J Am Chem Soc. 2001;123(6):1173-1183. google scholar
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- Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785-2791. google scholar
- Kim S, Chen J, Cheng T, et al. PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Res. 2021;49(D1):388-395. google scholar
- Behera SK, Vhora N, Contractor D, et al. Computational drug repurposing study elucidating simultaneous inhibition of entry and replication of novel corona virus by Grazoprevir. Sci Rep. 2021;11(1):7307. doi:10.1038/s41598-021-86712-2 google scholar
- Durrant JD, McCammon JA. Molecular dynamics simulations and drug discovery. BMC Biol. 2011;9:71. doi:10.1186/1741-7007-9-71 google scholar
- Raghu R, Devaraji V, Leena K, et al. Virtual screening and dis-covery of novel aurora kinase inhibitors. Curr Top Med Chem.2014;14(17):2006-2019. google scholar
- Shivakumar D, Williams J, Wu Y, Damm W, Shelley J, Sherman W. Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J Chem Theory Comput. 2010;6(5):1509-1519. google scholar
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- Deniz U, Ozkirimli E, Ulgen KO. A systematic methodology for large scale compound screening: A case study on the discovery of novel S1PL inhibitors. JMol Graph Model. 2016;63:110-124. google scholar
- Behera SK, Mahapatra N, Tripathy CS, Pati S. Drug repurpos-ing for identification of potential inhibitors against SARS-CoV-2 spike receptor-binding domain: An in silico approach. Indian J Med Res. 2021;153(1 & 2):132-143. google scholar
- Pace CN, Fu H, Lee Fryar K, et al. Contribution of hydrogen bonds to protein stability. Protein Sci. 2014;23(5):652-661. google scholar
- Vladilo G, Hassanali A. Hydrogen bonds and life in the universe. Life (Basel). 2018;8(1). doi:10.3390/life8010001 google scholar
- Bissantz C, Kuhn B, Stahl M. A medicinal chemist’s guide to molecular interactions. JMed Chem. 2010;53(14):5061-5084. google scholar
- Chen D, Oezguen N, Urvil P, Ferguson C, Dann SM, Savidge TC. Regulation of protein-ligand binding affinity by hydrogen bond pairing. Sci Adv. 2016;2(3):e1501240. doi:10.1126/sciadv.1501240 google scholar
- Hamelberg D, Mongan J, McCammon JA. Accelerated molec-ular dynamics: Apromising and efficient simulation method for biomolecules. J Chem Phys. 2004;120(24):11919-11929. google scholar
- Ferenczy GG, Kellermayer M. Contribution of hydrophobic inter-actions to protein mechanical stability. Comput Struct Biotechnol J. 2022;20:1946-1956. google scholar
- Dariya B, Behera SK, Srivani G, Farran B, Alam A, Nagaraju GP. Computational analysis of nuclear factor-kappaB and resveratrol in colorectal cancer. J Biomol Struct Dyn. 2021;39(8):2914-2922. google scholar
- Al-Karmalawy AA, Dahab MA, Metwaly AM, et al. Molec-ular docking and dynamics simulation revealed the poten-tial inhibitory activity of ACEIs against SARS-CoV-2 tar-geting the hACE2 receptor. Front Chem. 2021;9:661230. doi:10.3389/fchem.2021.661230 google scholar
- Morris JH, Meng EC, Ferrin TE. Computational tools for the interactive exploration of proteomic and structural data. Mol Cell Proteomics. 2010;9(8):1703-1715. google scholar
- Sliwoski G, Kothiwale S, Meiler J, Lowe EW, Jr. Computational methods in drug discovery. Pharmacol Rev. 2014;66(1):334-395. google scholar
- Sadybekov AV, Katritch V. Computational approaches streamlin-ing drug discovery. Nature. 2023;616(7958):673-685. google scholar
- Du X, Li Y, Xia YL, et al. Insights into protein-ligand interactions: Mechanisms, models, and methods. Int J Mol Sci. 2016;17(2). doi:10.3390/ijms17020144 google scholar
- Nagaraju GP, Farran B, Farren M, et al. Napabucasin (BBI 608), a potent chemoradiosensitizer in rectal cancer. Cancer. 2020;126(14):3360-3371. google scholar