An In-silico Screening Strategy to the Prediction of New Inhibitors of COVID-19 M-pro Protein

Abstract

The coronavims disease-2019 (COVID-19) was first recognized in Wuhan, China, and quickly spread worldwide. Between all proposed research guidelines, inhibition of the main protease M-pro) protein of the virus will be one of the main strategies for COVID-19 treatment. The present work was aimed to perform a computational study on FDA-approved drugs, similar to piperine scaffold, to find possible M-pro inhibitors. Firstly, virtual screening studies were performed on a library of FDA-approved drugs (43 medicinal compounds, similar to piperine scaffold). Among imported 43 drugs to virtual screening, 34 compounds were extracted. Four top-ranked drugs in terms of the highest interactions and the lowest binding energy were selected for the IFD study. Among these selections, lasofoxifene showed the lowest IFD score (-691.743 kcal mol(-1)). The stability of lasofoxifene in the COVID-19 M-pro protein active site was confirmed with 100 ns MD simulation. Lasofoxifene binding free energy was obtained -107.09 and -173.97 kcal mol(-1), using Prime MM-GBSA and gmmpbsa methods, respectively. The identified lasofoxifene by the presented computational approaches could be a suitable lead for inhibiting M-pro protein and COVID-19 treatment.