REVISITING THE SARS-COV-2 MAIN PROTEASE: A 2023 IN SILICO ODYSSEY IN SEARCH OF POTENTIAL INHIBITORS

Main Article Content

Chenyue Fan
Ayesha Abdul Qadir Memon
Prajit Adhikari
Muhammad Osama
Calvin R. Wei

Keywords

SARS-CoV-2, COVID-19, Mpro, Main Protease, Molecular Docking, Molecular Dynamics, ADMET, Toxicity, Drug Repurposing, Drug Discovery, Virtual Screening, Clinical, Trials

Abstract

The novel coronavirus disease or Covid-19 is a global pandemic caused by the SARS-CoV-2 virus originated from Wuhan, China in December 2019. A rapidly spreading, contagious virus that caused more than 6.7 million deaths worldwide. The main protease of SARS-CoV-2 is believed to play a vital role in mediating viral replication and transcription, making it a potential target of interest against Covid-19. In this study, virtual drug screening methods were conducted against a current Mpro structure (Protein Data Bank ID: 8SXR) with 868 ligands from the NIH Clinical Collection of clinical trial molecules. Multiple possible hit compounds were identified with compound 1 and 2 outperforming the other compounds in binding conformation and binding free energy. Toxicity and ADMET properties of the top 5 compounds were further investigated computationally. To further validate the results, molecular dynamic simulations of the top 2 complexes were performed. The two complexes displayed stable affinity in respect to the root mean square distance (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA) and hydrogen bond.

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References

1. Polatoğlu, I., Oncu‐Oner, T., Dalman, I. and Ozdogan, S., 2023. COVID‐19 in early 2023: Structure, replication mechanism, variants of SARS‐CoV‐2, diagnostic tests, and vaccine & drug development studies. MedComm, 4(2), p.e228.
2. Zhou, P., Yang, X.L., Wang, X.G., Hu, B., Zhang, L., Zhang, W., Si, H.R., Zhu, Y., Li, B., Huang, C.L. and Chen, H.D., 2020. A pneumonia outbreak associated with a new coronavirus of probable bat origin. nature, 579(7798), pp.270-273.
3. Pal, M., Berhanu, G., Desalegn, C. and Kandi, V., 2020. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2): an update. Cureus, 12(3).
4. Sadeghi Dousari, A., Taati Moghadam, M. and Satarzadeh, N., 2020. COVID-19 (Coronavirus disease 2019): a new coronavirus disease. Infection and drug resistance, pp.2819-2828.
5. Drożdżal, S., Rosik, J., Lechowicz, K., Machaj, F., Szostak, B., Przybyciński, J., Lorzadeh, S., Kotfis, K., Ghavami, S. and Łos, M.J., 2021. An update on drugs with therapeutic potential for SARS-CoV-2 (COVID-19) treatment. Drug Resistance Updates, 59, p.100794.
6. Wei, C.R., & Lang’at, G.C. (2023). In Silico Drug Discovery: The Next Frontier in the Fight Against SARS-CoV-2. Preprints. https://doi.org/10.20944/preprints202308.0887.v1
7. Zhou, Y.W., Xie, Y., Tang, L.S., Pu, D., Zhu, Y.J., Liu, J.Y. and Ma, X.L., 2021. Therapeutic targets and interventional strategies in COVID-19: mechanisms and clinical studies. Signal transduction and targeted therapy, 6(1), p.317.
8. Moustaqil, M., Ollivier, E., Chiu, H.P., Van Tol, S., Rudolffi-Soto, P., Stevens, C., Bhumkar, A., Hunter, D.J., Freiberg, A.N., Jacques, D. and Lee, B., 2021. SARS-CoV-2 proteases PLpro and 3CLpro cleave IRF3 and critical modulators of inflammatory pathways (NLRP12 and TAB1): implications for disease presentation across species. Emerging microbes & infections, 10(1), pp.178-195.
9. Jiang, H., Yang, P. and Zhang, J., 2022. Potential inhibitors targeting papain-like protease of SARS-CoV-2: two birds with one stone. Frontiers in chemistry, 10, p.822785.
10. Yang, H. and Rao, Z., 2021. Structural biology of SARS-CoV-2 and implications for therapeutic development. Nature Reviews Microbiology, 19(11), pp.685-700.
11. Toussi, S.S., Hammond, J.L., Gerstenberger, B.S. and Anderson, A.S., 2023. Therapeutics for COVID-19. Nature Microbiology, 8(5), pp.771-786.
12. Ashraf, F.B., Akter, S., Mumu, S.H., Islam, M.U. and Uddin, J., 2023. Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches. Plos one, 18(9), p.e0288053.
13. Pérez-Vargas, Jimena et al. A novel class of broad-spectrum active-site-directed 3C-like protease inhibitors with nanomolar antiviral activity against highly immune-evasive SARS-CoV-2 Omicron subvariants.” Emerging microbes & infections vol. 12,2 (2023): 2246594. doi:10.1080/ 22221751. 2023.2246594
14. Laskoswki, R.A., MacArthur, M.W., Moss, D.S., Thornton, J.M., 1993. Main-chain bond lengths and bond angles in protein structures. Journal of Molecular Biology, 231(4), pp.1049-67.
15. Wiederstein, M.; Sippl, M.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007, 35, W407–W410.
16. Schrödinger Release 2023-3: Maestro, Schrödinger, LLC, New York, NY, USA
17. Sastry, G.M.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W., "Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments," J. Comput. Aid. Mol. Des., 2013, 27(3), 221-234
18. Schrödinger Release 2023-3: Protein Preparation Wizard; Epik, Schrödinger, LLC, New York, NY, 2023; Impact, Schrödinger, LLC, New York, NY; Prime, Schrödinger, LLC, New York, NY, 2023.
19. Schrödinger Release 2023-3: BioLuminate, Schrödinger, LLC, New York, NY, 2023.
20. Beard, H.; Cholleti, A.; Pearlman, D.; Sherman, W.; Loving, K.A., "Applying Physics-Based Scoring to Calculate Free Energies of Binding for Single Amino Acid Mutations in Protein-Protein Complexes," PLoS ONE, 2013, 8(12), e82849
21. Salam, N.K.; Adzhigirey, M.; Sherman, W.; Pearlman, D.A., "Structure-based approach to the prediction of disulfide bonds in proteins," Protein Eng Des Sel, 2014, 27(10), 365-74
22. Zhu, K.; Day, T.; Warshaviak, D.; Murrett, C.; Friesner, R.; Pearlman, D., "Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction," Proteins, 2014, 82(8), 1646-1655
23. Schrödinger Release 2023-3: LigPrep, Schrödinger, LLC, New York, NY, USA
24. Schrödinger Release 2023-3: Glide, Schrödinger, LLC, New York, NY, 2023.
25. Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren,T. A.; Sanschagrin, P. C.; Mainz, D. T., "Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein-Ligand Complexes," J. Med. Chem., 2006, 49, 6177–6196
26. Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L., "Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening," J. Med. Chem., 2004, 47, 1750–1759
27. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shaw, D. E.; Shelley, M.; Perry, J. K.; Francis, P.; Shenkin, P. S., "Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy," J. Med. Chem., 2004, 47, 1739–1749
28. Xiong, Guoli et al. “ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties.” Nucleic acids research vol. 49,W1 (2021): W5-W14. doi:10.1093/nar/gkab255
29. Priyanka Banerjee, Andreas O Eckert, Anna K Schrey, Robert Preissner, ProTox-II: a webserver for the prediction of toxicity of chemicals, Nucleic Acids Research, Volume 46, Issue W1, 2 July 2018, Pages W257–W263
30. Malgorzata N. Drwal, Priyanka Banerjee, Mathias Dunkel, Martin R. Wettig, Robert Preissner, ProTox: a web server for the in silico prediction of rodent oral toxicity, Nucleic Acids Research, Volume 42, Issue W1, 1 July 2014, Pages W53–W58
31. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ. GROMACS: fast, flexible, and free. J Comput Chem. 2005 Dec;26(16):1701-18. doi: 10.1002/jcc.20291. PMID: 16211538.
32. D.A. Case, H.M. Aktulga, K. Belfon, I.Y. Ben-Shalom, J.T. Berryman, S.R. Brozell, D.S. Cerutti, T.E. Cheatham, III, G.A. Cisneros, V.W.D. Cruzeiro, T.A. Darden, N. Forouzesh, G. Giambaşu, T. Giese, M.K. Gilson, H. Gohlke, A.W. Goetz, J. Harris, S. Izadi, S.A. Izmailov, K. Kasavajhala, M.C. Kaymak, E. King, A. Kovalenko, T. Kurtzman, T.S. Lee, P. Li, C. Lin, J. Liu, T. Luchko, R. Luo, M. Machado, V. Man, M. Manathunga, K.M. Merz, Y. Miao, O. Mikhailovskii, G. Monard, H. Nguyen, K.A. O’Hearn, A. Onufriev, F. Pan, S. Pantano, R. Qi, A. Rahnamoun, D.R. Roe, A. Roitberg, C. Sagui, S. Schott-Verdugo, A. Shajan, J. Shen, C.L. Simmerling, N.R. Skrynnikov, J. Smith, J. Swails, R.C. Walker, J. Wang, J. Wang, H. Wei, X. Wu, Y. Wu, Y. Xiong, Y. Xue, D.M. York, S. Zhao, Q. Zhu, and P.A. Kollman (2023), Amber 2023, University of California, San Francisco.
33. Sousa da Silva AW, Vranken WF. ACPYPE - AnteChamber PYthon Parser interfacE. BMC Res Notes. 2012 Jul 23;5:367. doi: 10.1186/1756-0500-5-367.
34. Valdés-Tresanco MS, Valdés-Tresanco ME, Valiente PA, Moreno E. gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. J Chem Theory Comput. 2021 Oct 12;17(10):6281-6291. doi: 10.1021/acs.jctc.1c00645.
35. Abohassan M, Alshahrani M, Alshahrani MY, Rajagopalan P. Insilco and Invitro approaches identify novel dual PI3K/AKT pathway inhibitors to control acute myeloid leukemia cell proliferations. Med Oncol. 2022 Oct 8;39(12):249. doi: 10.1007/s12032-022-01846-1.
36. [Colovos, C, and T O Yeates. “Verification of protein structures: patterns of nonbonded atomic interactions.” Protein science : a publication of the Protein Society vol. 2,9 (1993): 1511-9. doi:10.1002/pro.5560020916
37. Kolodkin AN, Bruggeman FJ, Plant N, Moné MJ, Bakker BM, Campbell MJ, van Leeuwen JP, Carlberg C, Snoep JL, Westerhoff HV, Design principles of nuclear receptor signaling: how complex networking improves signal transduction. Mol Syst Biol. 6 (2010) 446 doi: 10.1038/msb.2010.102
38. Simmons SO, Fan C-Y, Ramabhadran R, Cellular stress response pathway system as a sentinel ensemble in toxicological screening. Toxicological Sciences 111(2) (2009) 202- 225
39. Kang KW, Lee SJ, Kim SG, Molecular mechanism of nrf2 activation by oxidative stress. Antioxid Redox Signal. 7 (2005) 1664-1673
40. Kensler TW, Wakabayashi N, Biswal S, Cell survival responses to environmental stresses via the Keap1-Nrf2-ARE pathway. Annu Rev Pharmacol Toxicol. 47 (2007) 89- 116
41. Hill S, Sataranatarajan K, Remmen HV, Role of signaling molecules in mitochondrial stress response. Front Genet. 9 (2018) 225 doi:10.3389/fgene.2018.00225
42. Parikh VS, Morgan MM, Scott R, Clements LS, Butow RA, The mitochondrial genotype can influence nuclear gene expression in yeast. Science 235(4788) (1987) 576- 580
43. Meyer JN, Hartman JH, Mello DF, Mitochondrial toxicity. Toxicological Sciences 162(1) (2018) 15-23
44. Richter U, Ng KY, Suomi F, Marttinen P, Turunen T, Jackson C, Suomalainen A, Vihinen H, Jokitalo E, Nyman TA, Isokallio MA, Stewart JB, Mancini C, Brusco A, Seneca S, Lombès A, Taylor RW, Battersby BJ, Mitochondrial stress response triggered by defects in protein synthesis quality control. Life Sci Alliance 2(1) (2019) e201800219 doi:10.26508/ lsa.201800219

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