Title : Virtual high throughput screening study to identify potential inhibitors from natural compounds against SARS-CoV-2 Mpro: A systematic molecular modelling approach
Abstract:
In 2020, COVID‐19 has created a major threat to human population across the world. In this pandemic, it is very difficult to discover novel drugs immediately. So, natural remedies for the prevention and treatment of COVID-19 has been widely accepted as the rapid way for effective therapeutic options which can be identified via in-silico drug screening experiments. The main protease (MPro) is an important drug target as it is essential and ubiquitous in SARS CoV-2 virus. In this study, we performed in-silico high-throughput virtual screening with library of 325,000 natural compounds from supernatural-II database to identify potential hits. We used 3D crystallographic Mpro protein (6XQS.pdb) structure to find the lead molecules. The initially obtained top 100 hits from VHTS were subjected to SP docking and the top 30 hits H1-H30 were further subjected to the extra-precision (XP) docking by using Glide module and also binding free energy calculations for final compounds were performed by prime MM-GBSA module of Schrodinger suit-2020. It is evident that Coulomb and van der Waals energy were major favourable contributors while electrostatic solvation energy term disfavours the binding of ligands to the Mpro target protein. The in-silico ADMET properties were predicted by using Qikprop, Chem Axon and data warrior tools which showed the favourable pharmacokinetic profile of natural compounds. In order to validate the stability of inhibitor-protein complex, compounds SN00340755 and SN00213037 with the highest inhibitory potential against Mpro and lowest binding free energy was subjected to 100-ns molecular dynamics simulation using Desmond module.
