Skip to main content

Ligand-Based Approaches for the Development of Drugs Against SARS-CoV-2

  • Protocol
  • First Online:
In Silico Modeling of Drugs Against Coronaviruses

Abstract

The current COVID-19 pandemic caused by SARS-CoV-2 has now seen an unprecedented global trend of viral transmission leading to over a million fatalities worldwide. SARS-CoV-2 is a betacoronavirus which possesses a single-stranded positive-sense RNA genome that encodes various structural, non-structural, and accessory proteins. Due to the zoonotic nature of SARS-CoV-2 and current transmission trend, scientists must identify effective therapeutics against the virus. Ligand-based drug designing is a computational approach based on the principle that similar compounds exhibit similar activities; hence, it is employed to identify, screen, or design drug-like molecules based on the existing drug molecules. The present chapter provides an overview of SARS-CoV-2, COVID-19, viral drug targets and dives deeply into the computational approach of ligand-based drug designing (LBDD). The chapter aims to provide a detailed methodology of LBDD and the current research endeavors that have utilized the technique to identify, screen, or design potential drug molecules against SARS-CoV-2.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Coronaviridae Study Group of the International Committee on Taxonomy of, V (2020) The species severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 5(4):536–544

    Article  CAS  Google Scholar 

  2. Lu R et al (2020) Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395(10224):565–574

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Fehr AR, Perlman S (2015) Coronaviruses: an overview of their replication and pathogenesis. Methods Mol Biol 1282:1–23

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yang N, Shen HM (2020) Targeting the endocytic pathway and autophagy process as a novel therapeutic strategy in COVID-19. Int J Biol Sci 16(10):1724–1731

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Chen Y, Liu Q, Guo D (2020) Emerging coronaviruses: genome structure, replication, and pathogenesis. J Med Virol 92(4):418–423

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Brian DA, Baric RS (2005) Coronavirus genome structure and replication. Curr Top Microbiol Immunol 287:1–30

    CAS  PubMed  Google Scholar 

  7. Pillaiyar T et al (2016) An overview of severe acute respiratory syndrome-coronavirus (SARS-CoV) 3CL protease inhibitors: peptidomimetics and small molecule chemotherapy. J Med Chem 59(14):6595–6628

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Knoops K et al (2008) SARS-coronavirus replication is supported by a reticulovesicular network of modified endoplasmic reticulum. PLoS Biol 6(9):e226

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Fan K et al (2004) Biosynthesis, purification, and substrate specificity of severe acute respiratory syndrome coronavirus 3C-like proteinase. J Biol Chem 279(3):1637–1642

    Article  CAS  PubMed  Google Scholar 

  10. Ou X et al (2020) Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV. Nat Commun 11(1):1620

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Jimenez-Guardeno JM et al (2014) The PDZ-binding motif of severe acute respiratory syndrome coronavirus envelope protein is a determinant of viral pathogenesis. PLoS Pathog 10(8):e1004320

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Chang CK et al (2014) The SARS coronavirus nucleocapsid protein--forms and functions. Antiviral Res 103:39–50

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Khailany RA, Safdar M, Ozaslan M (2020) Genomic characterization of a novel SARS-CoV-2. Gene Rep 19:100682

    Article  PubMed  PubMed Central  Google Scholar 

  14. de Wilde AH et al (2018) Host factors in coronavirus replication. Curr Top Microbiol Immunol 419:1–42

    PubMed  Google Scholar 

  15. Vellingiri B et al (2020) COVID-19: a promising cure for the global panic. Sci Total Environ 725:138277

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Huang C et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223):497–506

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Menni C et al (2020) Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat Med 26(7):1037–1040

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Adhikari SP et al (2020) Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infect Dis Poverty 9(1):29

    Article  PubMed  PubMed Central  Google Scholar 

  19. Prevention, C.f.D.C.a (2020) Testing for COVID-19. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/testing.html. Accessed 31 Oct 2020

  20. Dong E, Du H, Gardner L (2020) An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 20(5):533–534

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. WHO (2020) Estimating mortality from COVID-19. https://www.who.int/publications/i/item/WHO-2019-nCoV-Sci-Brief-Mortality-2020.1. Accessed 20 Oct 2020

  22. Ziebuhr J, Snijder EJ, Gorbalenya AE (2000) Virus-encoded proteinases and proteolytic processing in the Nidovirales. J Gen Virol 81(Pt 4):853–879

    Article  CAS  PubMed  Google Scholar 

  23. Lei J, Kusov Y, Hilgenfeld R (2018) Nsp3 of coronaviruses: structures and functions of a large multi-domain protein. Antiviral Res 149:58–74

    Article  CAS  PubMed  Google Scholar 

  24. Baez-Santos YM, St John SE, Mesecar AD (2015) The SARS-coronavirus papain-like protease: structure, function and inhibition by designed antiviral compounds. Antiviral Res 115:21–38

    Article  CAS  PubMed  Google Scholar 

  25. Rut W et al (2020) Activity profiling and structures of inhibitor-bound SARS-CoV-2-PLpro protease provides a framework for anti-COVID-19 drug design. BioRxiv

    Google Scholar 

  26. Baez-Santos YM et al (2014) X-ray structural and biological evaluation of a series of potent and highly selective inhibitors of human coronavirus papain-like proteases. J Med Chem 57(6):2393–2412

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Thiel V et al (2001) Viral replicase gene products suffice for coronavirus discontinuous transcription. J Virol 75(14):6676–6681

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Anand K et al (2003) Coronavirus main proteinase (3CLpro) structure: basis for design of anti-SARS drugs. Science 300(5626):1763–1767

    Article  CAS  PubMed  Google Scholar 

  29. Jin Z et al (2020) Structure of M(pro) from SARS-CoV-2 and discovery of its inhibitors. Nature 582(7811):289–293

    Article  CAS  PubMed  Google Scholar 

  30. Singh E et al (2020) A comprehensive review on promising anti-viral therapeutic candidates identified against main protease from SARS-CoV-2 through various computational methods. J Genet Eng Biotechnol 18(1):69

    Article  PubMed  PubMed Central  Google Scholar 

  31. Xue X et al (2008) Structures of two coronavirus main proteases: implications for substrate binding and antiviral drug design. J Virol 82(5):2515–2527

    Article  CAS  PubMed  Google Scholar 

  32. Ren Z et al (2013) The newly emerged SARS-like coronavirus HCoV-EMC also has an “Achilles’ heel”: current effective inhibitor targeting a 3C-like protease. Protein Cell 4(4):248–250

    Article  PubMed  PubMed Central  Google Scholar 

  33. Aftab SO et al (2020) Analysis of SARS-CoV-2 RNA-dependent RNA polymerase as a potential therapeutic drug target using a computational approach. J Transl Med 18(1):275

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yin W et al (2020) Structural basis for inhibition of the RNA-dependent RNA polymerase from SARS-CoV-2 by remdesivir. Science 368(6498):1499–1504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Shannon A et al (2020) Remdesivir and SARS-CoV-2: structural requirements at both nsp12 RdRp and nsp14 Exonuclease active-sites. Antiviral Res 178:104793

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. te Velthuis AJ, van den Worm SH, Snijder EJ (2012) The SARS-coronavirus nsp7+nsp8 complex is a unique multimeric RNA polymerase capable of both de novo initiation and primer extension. Nucleic Acids Res 40(4):1737–1747

    Article  CAS  Google Scholar 

  37. Gao Y et al (2020) Structure of the RNA-dependent RNA polymerase from COVID-19 virus. Science 368(6492):779–782

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ojha PK et al (2020) Therapeutics for COVID-19: from computation to practices-where we are, where we are heading to. Mol Divers:1–35

    Google Scholar 

  39. Sevajol M et al (2014) Insights into RNA synthesis, capping, and proofreading mechanisms of SARS-coronavirus. Virus Res 194:90–99

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Snijder EJ, Decroly E, Ziebuhr J (2016) The nonstructural proteins directing coronavirus RNA synthesis and processing. Adv Virus Res 96:59–126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ma Y et al (2015) Structural basis and functional analysis of the SARS coronavirus nsp14-nsp10 complex. Proc Natl Acad Sci U S A 112(30):9436–9441

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Viswanathan T et al (2020) Structural basis of RNA cap modification by SARS-CoV-2. Nat Commun 11(1):3718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang Y et al (2015) Coronavirus nsp10/nsp16 methyltransferase can be targeted by nsp10-derived peptide in vitro and in vivo to reduce replication and pathogenesis. J Virol 89(16):8416–8427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tang T et al (2020) Coronavirus membrane fusion mechanism offers a potential target for antiviral development. Antiviral Res 178:104792

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kandeel M et al (2020) From SARS and MERS CoVs to SARS-CoV-2: moving toward more biased codon usage in viral structural and nonstructural genes. J Med Virol 92(6):660–666

    Article  CAS  PubMed  Google Scholar 

  46. Hoffmann M et al (2020) SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181(2):271–280.e8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Heald-Sargent T, Gallagher T (2012) Ready, set, fuse! The coronavirus spike protein and acquisition of fusion competence. Viruses 4(4):557–580

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wang Q et al (2020) A unique protease cleavage site predicted in the spike protein of the novel pneumonia coronavirus (2019-nCoV) potentially related to viral transmissibility. Virol Sin 35(3):337–339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kortagere S (2013) In: Kortagere S (ed) In silico models for drug discovery, vol 993, 1st edn. Humana Press, Totowa, NJ

    Chapter  Google Scholar 

  50. Rush TS III et al (2005) A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. J Med Chem 48(5):1489–1495

    Article  CAS  PubMed  Google Scholar 

  51. Schwede T et al (2003) SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 31(13):3381–3385

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Eswar N et al (2006) Comparative protein structure modeling using Modeller. Curr Protoc Bioinformatics Chapter 5:Unit-5.6

    PubMed  Google Scholar 

  53. Guan L et al (2019) ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness. Med Chem Commun 10(1):148–157

    Article  CAS  Google Scholar 

  54. Benet LZ et al (2016) BDDCS, the rule of 5 and drugability. Adv Drug Deliv Rev 101:89–98

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Amera GM et al (2020) Computer aided ligand based screening for identification of promising molecules against enzymes involved in peptidoglycan biosynthetic pathway from Acinetobacter baumannii. Microb Pathog 147:104205

    Article  CAS  PubMed  Google Scholar 

  56. Forli S et al (2016) Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc 11(5):905–919

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Dallakyan S, Olson AJ (2015) Small-molecule library screening by docking with PyRx. Methods Mol Biol 1263:243–250

    Article  CAS  PubMed  Google Scholar 

  59. Zhu K et al (2014) Docking covalent inhibitors: a parameter free approach to pose prediction and scoring. J Chem Inf Model 54(7):1932–1940

    Article  CAS  PubMed  Google Scholar 

  60. Studio, D., Dassault systems BIOVIA (2016) Discovery Studio modelling environment, Release, 4. Dassault Systèmes, San Diego, CA

    Google Scholar 

  61. Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for all. Neuron 99(6):1129–1143

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9(9):646–652

    Article  CAS  PubMed  Google Scholar 

  63. Senn HM, Thiel W (2009) QM/MM methods for biomolecular systems. Angew Chem Int Ed Engl 48(7):1198–1229

    Article  CAS  PubMed  Google Scholar 

  64. Khan RJ et al (2020) Targeting SARS-CoV-2: a systematic drug repurposing approach to identify promising inhibitors against 3C-like proteinase and 2′-O-ribose methyltransferase. J Biomol Struct Dyn:1–14

    Google Scholar 

  65. Khan RJ et al (2020) Identification of promising antiviral drug candidates against non-structural protein 15 (NSP15) from SARS-CoV-2: an in silico assisted drug-repurposing study. J Biomol Struct Dyn:1–11

    Google Scholar 

  66. Hofmarcher M et al (2020) Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks. SSRN Electron J

    Google Scholar 

  67. De P et al (2020) In silico modeling for quick prediction of inhibitory activity against 3CL(pro) enzyme in SARS CoV diseases. J Biomol Struct Dyn:1–27

    Google Scholar 

  68. Khan PM, Kumar V, Roy K (2020) In silico modeling of small molecule carboxamides as inhibitors of SARS-CoV 3CL protease: an approach towards combating COVID-19. Comb Chem High Throughput Screen

    Google Scholar 

  69. Kumar V, Roy K (2020) Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3C-like protease (3CLpro) enzyme inhibitors against SARS-CoV diseases. SAR QSAR Environ Res 31(7):511–526

    Article  CAS  PubMed  Google Scholar 

  70. Amin SA et al (2020) Chemical-informatics approach to COVID-19 drug discovery: Monte Carlo based QSAR, virtual screening and molecular docking study of some in-house molecules as papain-like protease (PLpro) inhibitors. J Biomol Struct Dyn:1–10

    Google Scholar 

  71. Ferraz WR et al (2020) Ligand and structure-based virtual screening applied to the SARS-CoV-2 main protease: an in silico repurposing study. Future Med Chem 12(20):1815–1828

    Article  CAS  PubMed  Google Scholar 

  72. Kumar N et al (2020) Antitussive noscapine and antiviral drug conjugates as arsenal against COVID-19: a comprehensive chemoinformatics analysis. J Biomol Struct Dyn:1–16

    Google Scholar 

  73. Rahman Oany A et al (2020) Design of novel viral attachment inhibitors of the spike glycoprotein (S) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) through virtual screening and dynamics. Int J Antimicrob Agents:106177

    Google Scholar 

Download references

Acknowledgments

Dr. Amit Kumar Singh thanks Indian Council of Medical Research (ICMR) and Indian National Science Academy (INSA), New Delhi, India. Gizachew Muluneh Amera thanks the College of Natural Science, Wollo University, Dessie, Ethiopia for the sponsorship. The authors thank Sharda University, Greater Noida, India, for support.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jayaraman Muthukumaran or Amit Kumar Singh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Singh, E. et al. (2021). Ligand-Based Approaches for the Development of Drugs Against 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_65

Download citation

  • DOI: https://doi.org/10.1007/7653_2020_65

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1365-8

  • Online ISBN: 978-1-0716-1366-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics