Structure-based virtual screening and molecular dynamics simulation studies to discover new SARS-CoV-2 main protease inhibitors

Abstract

Computational methods were used to filter two datasets (> 8,000 compounds) based on two criteria: higher binding affinity for MPRO than cocrystallized inhibitor and binding interactions with MPRO catalytic dyad (Cys145 and His41). After virtual screening involving ranking and reranking, eleven compounds were identified to satisfy these criteria and analysis of their structures revealed an unparallel common features among them which could be critical for their interactions with MPRO. However, only the topmost scoring compound (AV-203: Ki = 0.31 µM) exhibited relatively stable binding interaction during the period of 50 ns MD simulation and thus is a suitable template for drug development.

Keywords

• Coronavirus
• SARS-CoV-2
• Main protease inhibitor
• Docking
• Molecular dynamics
• Binding mode prediction