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Molecular Simulation–Driven Drug Repurposing for the Identification of Inhibitors Against Non-Structural Proteins of SARS-CoV-2

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Book cover In Silico Modeling of Drugs Against Coronaviruses

Part of the book series: Methods in Pharmacology and Toxicology ((MIPT))

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Abstract

The SARS-CoV-2 pandemic (COVID-19) created an urgency to find a potential drug molecule for its cure. Repurposing of FDA-approved drugs facilitated by preliminary computational screening followed by experimental validation is now a well-established drug discovery protocol for attempting to find an effective cure against COVID-19 in a limited time with a lower risk of toxicity and higher efficacy. In this study, we identified computationally a few drugs showing good molecular interactions with known targets of SARS-CoV-2. Simulation studies are performed on 50 docked protein–drug complexes. The top 16 drugs (DB00198, DB00224, DB00503, DB00811, DB01098, DB01601, DB02701, DB04703, DB06159, DB06290, DB08889, DB09027, DB09297, DB13751, DB13814, and DB15623) are proposed as potential drug candidates for further experimental assessment to pick and choose drugs that could contain the virus and combat the pandemic. These 16 FDA-approved drug molecules are presented in the context of the vast emerging literature on drug repurposing to eliminate the novel coronavirus.

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References

  1. Cascella M, Rajnik M, Cuomo A, Dulebohn SC, Di Napoli R (2020) Features, evaluation and treatment coronavirus (COVID-19). In: Statpearls. Publishing, StatPearls

    Google Scholar 

  2. Chan JFW, Kok KH, Zhu Z, Chu H, To KKW, Yuan S, Yuen KY (2020) Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan. Emerg Microb Infect 9(1):221–236. https://doi.org/10.1080/22221751.2020.1719902

    Article  CAS  Google Scholar 

  3. Gordon DE, Jang GM, Bouhaddou M, Xu J, Obernier K, White KM et al (2020) A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 583:459–468. https://doi.org/10.1038/s41586-020-2286-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gil C, Ginex T, Maestro I, Nozal V, Barrado-Gil L, Cuesta-Geijo MA et al (2020) COVID-19: drug targets and potential treatments. J Med Chem 63:12359. https://doi.org/10.1021/acs.jmedchem.0c00606

    Article  CAS  PubMed  Google Scholar 

  5. https://www.biopharmadive.com/news/new-drug-cost-research-development-market-jama-study/573381/. Accessed 26 Sep 2020

  6. Osakwe O, Rizvi SA (2016) Social aspects of drug discovery, development and commercialization. Academic Press, New York, NY. https://doi.org/10.1016/B978-0-12-802220-7.00017-X

    Book  Google Scholar 

  7. Simsek M, Meijer B, van Bodegraven AA, de Boer NK, Mulder CJ (2018) Finding hidden treasures in old drugs: the challenges and importance of licensing generics. Drug Discov Today 23(1):17–21. https://doi.org/10.1016/j.drudis.2017.08.008

    Article  PubMed  Google Scholar 

  8. Talevi A, Bellera CL (2020) Challenges and opportunities with drug repurposing: finding strategies to find alternative uses of therapeutics. Expert Opin Drug Discovery 15:397. https://doi.org/10.1080/17460441.2020.1704729

    Article  Google Scholar 

  9. Dhir N, Jain A, Mahendru D, Prakash A, Medhi B (2020) Drug repurposing and orphan disease therapeutics. In: Drug repurposing. IntechOpen, Rijeka. https://doi.org/10.5772/intechopen.91941

    Chapter  Google Scholar 

  10. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov/solidarity-clinical-trial-for-covid-19-treatments

  11. Singh AK, Singh A, Shaikh A, Singh R, Misra A (2020) Chloroquine and hydroxychloroquine in the treatment of COVID-19 with or without diabetes: a systematic search and a narrative review with a special reference to India and other developing countries. Diabetes Metab Synd 14:214. https://doi.org/10.1016/j.dsx.2020.03.011

    Article  Google Scholar 

  12. Malek AE, Granwehr B, Kontoyiannis DP (2020) Doxycycline as a potential partner of COVID-19 therapies. IDCases 21:e00864. https://doi.org/10.1016/j.idcr.2020.e00864

    Article  PubMed  PubMed Central  Google Scholar 

  13. Caly L, Druce JD, Catton MG, Jans DA, Wagstaff KM (2020) The FDA-approved drug ivermectin inhibits the replication of SARS-CoV-2 in vitro. Antiviral Res 178:104787. https://doi.org/10.1016/j.antiviral.2020.104787

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yang JW, Yang L, Luo RG, Xu JF (2020) Corticosteroid administration for viral pneumonia: COVID-19 and beyond. Clin Microbiol Infect 26:1171. https://doi.org/10.1016/j.cmi.2020.06.020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Fu B, Xu X, Wei H (2020) Why tocilizumab could be an effective treatment for severe COVID-19? J Transl Med 18(1):1–5. https://doi.org/10.1186/s12967-020-02339-3

    Article  CAS  Google Scholar 

  16. Pizzorno A, Padey B, Dubois J, Julien T, Traversier A, Dulière V et al (2020) In vitro evaluation of antiviral activity of single and combined repurposable drugs against SARS-CoV-2. Antiviral Res 181:104878. https://doi.org/10.1016/j.antiviral.2020.104878

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Rossignol JF (2016) Nitazoxanide, a new drug candidate for the treatment of Middle East respiratory syndrome coronavirus. J Infect Public Health 9(3):227–230. https://doi.org/10.1016/j.jiph.2016.04.001

    Article  PubMed  PubMed Central  Google Scholar 

  18. Parvathaneni V, Gupta V (2020) Utilizing drug repurposing against COVID-19–efficacy, limitations, and challenges. Life Sci 259:118275. https://doi.org/10.1016/j.lfs.2020.118275

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. https://covid19-hpc-consortium.org/who-we-are

  20. Rudrapal M, Khairnar SJ, Jadhav AG (2020) Drug repurposing (DR): an emerging approach in drug discovery. In: Drug repurposing. IntechOpen, Rijeka. https://doi.org/10.5772/intechopen.93193

    Chapter  Google Scholar 

  21. Jarada TN, Rokne JG, Alhajj R (2020) A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. Journal of Cheminformatics 12(1):1–23. https://doi.org/10.1186/s13321-020-00450-7

    Article  CAS  Google Scholar 

  22. Elfiky AA (2020) Anti-HCV, nucleotide inhibitors, repurposing against COVID-19. Life Sci 248:117477. https://doi.org/10.1016/j.lfs.2020.117477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chakraborti S, Bheemireddy S, Srinivasan N (2020) Repurposing drugs against main protease of SARS-CoV-2: mechanism based insights supported by available laboratory and clinical data. Mol Omics 16:474. https://doi.org/10.1039/D0MO00057D

    Article  CAS  PubMed  Google Scholar 

  24. Kandeel M, Al-Nazawi M (2020) Virtual screening and repurposing of FDA approved drugs against COVID-19 main protease. Life Sci 251:117627. https://doi.org/10.1016/j.lfs.2020.117627

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Shah B, Modi P, Sagar SR (2020) In silico studies on therapeutic agents for COVID-19: drug repurposing approach. Life Sci 252:117652. https://doi.org/10.1016/j.lfs.2020.117652

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wang J (2020) Fast identification of possible drug treatment of coronavirus disease-19 (COVID-19) through computational drug repurposing study. J Chem Inf Model 60:3277. https://doi.org/10.1021/acs.jcim.0c00179

    Article  CAS  PubMed  Google Scholar 

  27. Muralidharan N, Sakthivel R, Velmurugan D, Gromiha MM (2020) Computational studies of drug repurposing and synergism of lopinavir, oseltamivir and ritonavir binding with SARS-CoV-2 Protease against COVID-19. J Biomol Struct Dyn:1–6. https://doi.org/10.1080/07391102.2020.1752802

  28. Elmezayen AD, Al-Obaidi A, Şahin AT, Yelekçi K (2020) Drug repurposing for coronavirus (COVID-19): in silico screening of known drugs against coronavirus 3CL hydrolase and protease enzymes. J Biomol Struct Dyn:1–13. https://doi.org/10.1080/07391102.2020.1758791

  29. Farag A, Wang P, Ahmed M, Sadek H (2020) Identification of FDA approved drugs targeting COVID-19 virus by structure-based drug repositioning. ChemRxiv. https://doi.org/10.26434/chemrxiv.12003930.v1

  30. Mahdian S, Ebrahim-Habibi A, Zarrabi M (2020) Drug repurposing using computational methods to identify therapeutic options for COVID-19. J Diabetes Metab Disord:1–9. https://doi.org/10.1007/s40200-020-00546-9

  31. Sharma A, Tiwari V, Sowdhamini R, Campus GKVK (2020) Computational Search for Potential COVID-19 Drugs from FDA-approved drugs and small molecules of natural origin identifies several anti-virals and plant products. J Biosci 45:100. https://doi.org/10.1007/s12038-020-00069-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. MOE (2011) Molecular Operating Environment 2011.10. Chemical Computing Group Inc, Montreal, QC

    Google Scholar 

  34. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749. https://doi.org/10.1021/jm0306430

    Article  CAS  PubMed  Google Scholar 

  35. Gupta A, Gandhimathi A, Sharma P, Jayaram B (2007) ParDOCK: an all atom energy based Monte Carlo docking protocol for protein-ligand complexes. Protein Pept Lett 14(7):632–646. https://doi.org/10.2174/092986607781483831

    Article  CAS  PubMed  Google Scholar 

  36. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242. https://doi.org/10.1093/nar/28.1.235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46(D1):D1074–D1082. https://doi.org/10.1093/nar/gkx1037

    Article  CAS  PubMed  Google Scholar 

  38. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46(W1):W296–W303. https://doi.org/10.1093/nar/gky427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Case DA, Babin V, Berryman J, Betz RM, Cai Q, Cerutti DS et al (2014) Amber 14. University of California, San Francisco, CA

    Google Scholar 

  40. Jayaram B, Singh T, Mukherjee G, Mathur A, Shekhar S, Shekhar V (2012) Sanjeevini: a freely accessible web-server for target directed lead molecule discovery. BMC Bioinformatics 13(S17):S7. https://doi.org/10.1186/1471-2105-13-S17-S7

    Article  PubMed  PubMed Central  Google Scholar 

  41. Jain T, Jayaram B (2007) Computational protocol for predicting the binding affinities of zinc containing metalloprotein–ligand complexes. Proteins 67(4):1167–1178. https://doi.org/10.1002/prot.21332

    Article  CAS  PubMed  Google Scholar 

  42. Soni A, Bhat R, Jayaram B (2020) Improving the binding affinity estimations of protein–ligand complexes using machine-learning facilitated force field method. J Comput Aided Mol Des 34:817–830. https://doi.org/10.1007/s10822-020-00305-1

    Article  CAS  PubMed  Google Scholar 

  43. Bhat R, Jayaraj A, Soni A, Jayaram B (2020) An overview of protein–ligand docking and scoring algorithms. In: Protein interactions: computational methods, analysis and applications. World Scientific, Singapore, p 371. https://doi.org/10.1142/9789811211874_0015

    Chapter  Google Scholar 

  44. Case DA et al (2018) AMBER 2018. University of California, San Francisco, CA

    Google Scholar 

  45. Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J Comput Chem 23(16):1623–1641. https://doi.org/10.1002/jcc.10128

    Article  CAS  PubMed  Google Scholar 

  46. Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11(8):3696–3713. https://doi.org/10.1021/acs.jctc.5b00255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174. https://doi.org/10.1002/jcc.20035

    Article  CAS  PubMed  Google Scholar 

  48. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935. https://doi.org/10.1063/1.445869

    Article  CAS  Google Scholar 

  49. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: An N⋅log (N) method for Ewald sums in large systems. J Chem Phys 98(12):10089–10092. https://doi.org/10.1063/1.464397

    Article  CAS  Google Scholar 

  50. Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690. https://doi.org/10.1063/1.448118

    Article  CAS  Google Scholar 

  51. Ryckaert JP, Ciccotti G, Berendsen HJ (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23(3):327–341. https://doi.org/10.1016/0021-9991(77)90098-5

    Article  CAS  Google Scholar 

  52. Srinivasan J, Miller J, Kollman PA, Case DA (1998) Continuum solvent studies of the stability of RNA hairpin loops and helices. J Biomol Struct Dyn 16(3):671–682. https://doi.org/10.1080/07391102.1998.10508279

    Article  CAS  PubMed  Google Scholar 

  53. Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L et al (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33(12):889–897. https://doi.org/10.1021/ar000033j

    Article  CAS  PubMed  Google Scholar 

  54. Srinivasan J, Cheatham TE, Cieplak P, Kollman PA, Case DA (1998) Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate – DNA helices. J Am Chem Soc 120(37):9401–9409. https://doi.org/10.1021/ja981844+

    Article  CAS  Google Scholar 

  55. Gilson MK, Honig B (1988) Calculation of the total electrostatic energy of a macromolecular system: solvation energies, binding energies, and conformational analysis. Proteins 4(1):7–18. https://doi.org/10.1002/prot.340040104

    Article  CAS  PubMed  Google Scholar 

  56. Wang J, Hou T, Xu X (2006) Recent advances in free energy calculations with a combination of molecular mechanics and continuum models. Curr Comput Aided Drug Des 2(3):287–306. https://doi.org/10.2174/157340906778226454

    Article  CAS  Google Scholar 

  57. Chang CE, Chen W, Gilson MK (2005) Evaluating the accuracy of the quasiharmonic approximation. J Chem Theory Comput 1(5):1017–1028. https://doi.org/10.1021/ct0500904

    Article  CAS  PubMed  Google Scholar 

  58. Genheden S, Ryde U (2012) Comparison of end-point continuum-solvation methods for the calculation of protein–ligand binding free energies. Proteins 80(5):1326–1342. https://doi.org/10.1002/prot.24029

    Article  CAS  PubMed  Google Scholar 

  59. Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng Des Select 8(2):127–134. https://doi.org/10.1093/protein/8.2.127

    Article  CAS  Google Scholar 

  60. PyMOL (2010) The PyMOL molecular graphics system. Version, 1(5). Schrodinger, L. L. C, New York, NY

    Google Scholar 

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Acknowledgments

This project is supported by the National Supercomputing Mission operated through MEITY and CDAC of Government of India and Department of Biotechnology, Government of India.

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Authors and Affiliations

  1. Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, India

    Amita Pathak & B. Jayaram

  2. Supercomputing Facility for Bioinformatics & Computational Biology, Indian Institute of Technology Delhi, New Delhi, India

    Amita Pathak, Bhumika Singh, Dheeraj Kumar Chaurasia & B. Jayaram

  3. Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, New Delhi, India

    B. Jayaram

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  1. Amita Pathak
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  2. Bhumika Singh
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  3. Dheeraj Kumar Chaurasia
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  4. B. Jayaram
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Correspondence to B. Jayaram .

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Editors and Affiliations

  1. Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India

    Kunal Roy

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Pathak, A., Singh, B., Chaurasia, D.K., Jayaram, B. (2021). Molecular Simulation–Driven Drug Repurposing for the Identification of Inhibitors Against Non-Structural Proteins of SARS-CoV-2. In: Roy, K. (eds) In Silico Modeling of Drugs Against Coronaviruses. Methods in Pharmacology and Toxicology. Humana, New York, NY. https://doi.org/10.1007/7653_2020_61

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  • DOI: https://doi.org/10.1007/7653_2020_61

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1365-8

  • Online ISBN: 978-1-0716-1366-5

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