In silico evaluation of NO donor heterocyclic vasodilators as SARS-CoV-2 Mpro protein inhibitor

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which causes COVID-19 disease has been exponentially increasing throughout the world. The mortality rate is increasing gradually as effective treatment is unavailable to date. In silico based screening for novel testable hypotheses on SARS-CoV-2 M^pro protein to discover the potential lead drug candidate is an emerging area along with the discovery of a vaccine. Administration of NO-releasing agents, NO inducers or the NO gas itself may be useful as therapeutics in the treatment of SARS-CoV-2. In the present study, a 3D structure of SARS-CoV-2 M^pro protein was used for the rational setting of inhibitors to the binding pocket of enzyme which proposed that phenyl furoxan derivative gets efficiently dock in the target pocket. Molecular docking and molecular dynamics simulations helped to investigate possible effective inhibitor candidates bound to SARS-CoV-2 M^pro substrate binding pocket. Molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations revealed energetic contributions of active site residues of M^pro in binding with most stable proposed NO donor heterocyclic vasodilator inhibitor molecules. Furthermore, principal component analysis (PCA) showed that the NO donor heterocyclic inhibitor molecules 14, 16, 18 and 19 was strongly bound to catalytic core of SARS-CoV-2 M^pro protein, limiting its movement to form stable complex as like control. Thus, overall in silico investigations revealed that 5-oxopiperazine-2-carboxylic acid coupled furoxan derivatives was found to be key pharmacophore in drug design for the treatment of SARS-CoV-2, a global pandemic disease with a dual mechanism of action as NO donor and a worthwhile ligand to act as SARS-CoV-2 M^pro protein inhibitor.Communicated by Ramaswamy H. Sarma.

Keywords: COVID-19; SARS-CoV-2 Mpro inhibition; molecular docking; molecular dynamics simulations; nitric oxide; phenyl furoxan; principal component analysis.