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

The coronavirus 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 g_mmpbsa methods, respectively. The identified lasofoxifene by the presented computational approaches could be a suitable lead for inhibiting M(pro) protein and COVID-19 treatment.