In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation.

Abstract

The incidence of 2019 novel corona virus (SARS-CoV-2) has created a medical emergency throughout the world. Various efforts have been made to develop the vaccine or effective treatments against the disease. The discovery of crystal structure of SARS-CoV-2 main protease has made the in silico identification of its inhibitors possible. Based on its critical role in viral replication, the viral protease can prove to be a promising “target” for antiviral drug therapy. We have systematically screened an in-house library of 15,754 natural and synthetic compounds, established at International Center for Chemical and Biological Sciences, University of Karachi. The in silico search for potential viral protease inhibitors resulted in nine top ranked ligands (compounds 1–9) against SARS-CoV-2 main protease (PDB ID: 6LU7) based on docking scores, and predictive binding energies. The in silico studies were updated via carrying out the docking, and predictive binding energy estimation, with a recently reported crystal structure of main protease (PDB ID: 6Y2F) at a better resolution i.e., 1.95 Å. Compound 2 (molecular bank code AAA396) was found to have highest negative binding energy of −71.63 kcal/mol for 6LU7. While compound 3 (molecular bank code AAD146) exhibited highest negative binding energy of -81.92 kcal/mol for 6Y2F. The stability of the compounds- in complex with viral protease was analyzed by Molecular Dynamics simulation studies, and was found to be stable over the course of 20 ns simulation time. Compound 2, and 3 were predicted to be the significant inhibitors of SARS-CoV-2 3CL hydrolase (Mpro) among the nine short listed compounds.