In Silico Prediction of Novel Inhibitors of SARS-CoV-2 Main Protease through Structure-Based Virtual Screening and Molecular Dynamic Simulation

The unprecedented pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is threatening global health. SARS-CoV-2 has caused severe disease with significant mortality since December 2019. The enzyme chymotrypsin-like protease (3CLpro) or main protease (M(pro)) of the virus is considered to be a promising drug target due to its crucial role in viral replication and its genomic dissimilarity to human proteases. In this study, we implemented a structure-based virtual screening (VS) protocol in search of compounds that could inhibit the viral M(pro). A library of >eight hundred compounds was screened by molecular docking into multiple structures of M(pro), and the result was analyzed by consensus strategy. Those compounds that were ranked mutually in the ‘Top-100’ position in at least 50% of the structures were selected and their analogous binding modes predicted simultaneously in all the structures were considered as bioactive poses. Subsequently, based on the predicted physiological and pharmacokinetic behavior and interaction analysis, eleven compounds were identified as ‘Hits’ against SARS-CoV-2 M(pro). Those eleven compounds, along with the apo form of M(pro) and one reference inhibitor (X77), were subjected to molecular dynamic simulation to explore the ligand-induced structural and dynamic behavior of M(pro). The MM-GBSA calculations reflect that eight out of eleven compounds specifically possess high to good binding affinities for M(pro). This study provides valuable insights to design more potent and selective inhibitors of SARS-CoV-2 M(pro).