Investigating the binding affinity, interaction, and structure-activity-relationship of 76 prescription antiviral drugs targeting RdRp and Mpro of SARS-CoV-2

Abstract

SARS-CoV-2 virus outbreak poses a major threat to humans worldwide due to its highly contagious nature. In this study, molecular docking, molecular dynamics, and structure-activity relationship are employed to assess the binding affinity and interaction of 76 prescription drugs against RNA dependent RNA polymerase (RdRp) and Main Protease (Mpro) of SARS-CoV-2. The RNA-dependent RNA polymerase is a vital enzyme of coronavirus replication/transcription complex whereas the main protease acts on the proteolysis of replicase polyproteins. Among 76 prescription antiviral drugs, four drugs (Raltegravir, Simeprevir, Cobicistat, and Daclatasvir) that are previously used for human immunodeficiency virus (HIV), hepatitis C virus (HCV), Ebola, and Marburg virus show higher binding energy and strong interaction with active sites of the receptor proteins. To explore the dynamic nature of the interaction, 100 ns molecular dynamics (MD) simulation is performed on the selected protein-drug complexes and apo-protein. Binding free energy of the selected drugs is performed by MM/PBSA. Besides docking and dynamics, partial least square (PLS) regression method is applied for the quantitative structure activity relationship to generate and predict the binding energy for drugs. PLS regression satisfactorily predicts the binding energy of the effective antiviral drugs compared to binding energy achieved from molecular docking with a precision of 85%. This study highly recommends researchers to screen these potential drugs in vitro and in vivo against SARS-CoV-2 for further validation of utility.

GRAPHICAL ABSTRACT

Communicated by Ramaswamy H. Sarma

Keywords:

• Antiviral drugs
• SARS-CoV-2
• molecular docking
• molecular dynamics
• quantitative structure-activity relationship
• principal component analysis

Acknowledgements

We are grateful to our donors (http://grc-bd.org/donate/) who supported to build a computational platform. The authors like to acknowledge The World Academy of Science (TWAS) to purchase the High-Performance Computers for molecular dynamics simulation.

Disclosure statement

The authors declare no competing financial interest.