Title : A systematic molecular modelling approach to identify potential inhibitors from natural compounds against SARS-CoV-2 Omicron
COVID‐19 has created a major threat to the human population across the globe. Since 2020, there are many mutations in SARS CoV-2 like Alpha, Beta, Delta and Omicron etc. Out of this Omicron variant is one of the fastest-spreading diseases in Covid-19. But still, we are not known about the effectiveness of vaccines and drugs against all the variants. So, natural remedies for the prevention and treatment of COVID-19 have been widely accepted as the rapid way for effective therapeutic options without major side effects, which can be identified via in-silico drug screening experiments. In this study, we performed in-silico high-throughput virtual screening with library of 325,000 natural compounds from the supernatural-II database to identify potential hits. We used 3D crystallographic Omicron protein (7TVX.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-2021-4. It is evident that Coulomb and van der Waals energy were major favorable contributors while the electrostatic solvation energy term disfavors the binding of ligands to the Omicron target protein. The in-silico ADMET properties were predicted by using Qikprop, Chem Axon and data warrior tools which showed the favorable pharmacokinetic profile of natural compounds. In order to validate the stability of the inhibitor-protein complex, compound SN000299979 with the highest inhibitory potential against Mpro and lowest binding free energy was subjected to 100-ns molecular dynamics simulation using the Desmond module.
Audience Take Away Notes:
- This research work explains Virtual High throughput screening of natural products against SARS CoV-2 Omicron target.
- This research that other faculty could use to expand their research or academics.
- This research work explains the docking, ADMET, and molecular dynamics study of the natural hits.
- This research work will be helpful to design novel molecules against COVID-19.