Remdesivir analogs against SARS-CoV-2 RNA-dependent RNA polymerase 

Abstract

The COVID-19 pandemic has already taken many lives but is still continuing its spread and exerting jeopardizing effects. This study is aimed to find the most potent ligands from 703 analogs of remdesivir against RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus . RdRp is a major part of a multi-subunit transcription complex of the virus, which is essential for viral replication. In clinical trials, it has been found that remdesivir is effective to inhibit viral replication in Ebola and in primary human lung cell cultures; it effectively impedes replication of a broad-spectrum pre-pandemic bat coronaviruses and epidemic human coronaviruses. After virtual screening, 30 most potent ligands and remdesivir were modified with triphosphate. Quantum mechanics-based quantitative structure–activity relationship envisages the binding energy for ligands applying partial least square (PLS) regression. PLS regression remarkably predicts the binding energy of the effective ligands with an accuracy of 80% compared to the value attained from molecular docking. Two ligands (L4:58059550 and L28:126719083), which have more interactions with the target protein than the other ligands including standard remdesivir triphosphate, were selected for further analysis. Molecular dynamics simulation is done to assess the stability and dynamic nature of the drug–protein complex. Binding-free energy results via PRODIGY server and molecular mechanics/Poisson-Boltzmann surface area method depict that the potential and solvation energies play a crucial role. Considering all computational analysis, we recommend the best remdesivir analogs can be utilized for efficacy test through in vitro and in vivo trials against SARS-CoV-2.

Communicated by Ramaswamy H. Sarma

Keywords:

• RNA-dependent RNA polymerase
• SARS-CoV-2
• molecular docking
• quantitative structure–activity relationship
• molecular dynamic simulation

Acknowledgments

We are grateful to The World Academy of Science (TWAS) to purchase the High-Performance Computers for molecular dynamics simulation. The authors wish to gratefully acknowledge and thank Dr. Mohammad A. Halim (Fort Smith, USA) for his kind support to this research.

Disclosure statement

No potential conflict of interest was reported by the authors.