Searching for potential drugs against SARS-CoV-2 through virtual screening on several molecular targets

J Biomol Struct Dyn. 2022 Jul;40(11):5229-5242. doi: 10.1080/07391102.2020.1869096. Epub 2021 Jan 8.

Abstract

The acute respiratory syndrome caused by the SARS-CoV-2, known as COVID-19, has been ruthlessly tormenting the world population for more than six months. However, so far no effective drug or vaccine against this plague have emerged yet, despite the huge effort in course by researchers and pharmaceutical companies worldwide. Willing to contribute with this fight to defeat COVID-19, we performed a virtual screening study on a library containing Food and Drug Administration (FDA) approved drugs, in a search for molecules capable of hitting three main molecular targets of SARS-CoV-2 currently available in the Protein Data Bank (PDB). Our results were refined with further molecular dynamics (MD) simulations and MM-PBSA calculations and pointed to 7 multi-target hits which we propose here for experimental evaluation and repurposing as potential drugs against COVID-19. Additional rounds of docking, MD simulations and MM-PBSA calculations with remdesivir suggested that this compound can also work as a multi-target drug against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Keywords: SARS-CoV-2; drug repurposing; multi-target approach; virtual screening.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 Drug Treatment*
  • Coronavirus 3C Proteases
  • Cysteine Endopeptidases
  • Humans
  • Molecular Docking Simulation
  • Molecular Dynamics Simulation
  • Pharmaceutical Preparations
  • Protease Inhibitors
  • SARS-CoV-2*

Substances

  • Pharmaceutical Preparations
  • Protease Inhibitors
  • Cysteine Endopeptidases
  • Coronavirus 3C Proteases

Grants and funding

The authors wish to thank the financial support of the Brazilian agencies Conselho Nacional de Pesquisa (CNPq), grant number 308225/2018–0, Fundação de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ), grant number E-02/202.961/2017, IFES—PRPPG, grant number 10/2019 (Productivity Researcher Program PPP); and FAPES, grant number 03/2020-2020-WMT5F. This work was also supported by the University of Hradec Kralove (VT2019-2021). We also thanks the Federal University of Lavras (UFLA) for software facilities.