Evaluating the structure-based virtual screening performance of SARS-CoV-2 main protease: A benchmarking approach and a multistage screening example against the wild-type and Omicron variants

Abstract

COVID-19 still poses a worldwide health threat due to continuous viral mutations and potential resistance to vaccination. SARS-CoV-2 viral multiplication hindrance by inhibiting the viral main protease (Mpro) deemed propitious. Structure-based virtual screening (SBVS) is a conventional strategy for discovering new inhibitors. Nonetheless, the SBVS efforts against Mpro variants needed to be benchmarked. Herein, in the first stage of the study, we evaluated four docking tools (FRED, PLANTS, AutoDock Vina and CDOCKER) via an in-depth benchmarking approach against SARS-CoV2 Mpro of both the wild type (WTMpro) and the deadly Omicron P132H variant (OMpro). We started by compiling an active dataset of non-covalent small molecule inhibitors of the WTMpro from literature and the COVID-Moonshot database along with generating a high-quality benchmark set via DEKOIS 2.0. pROC-Chemotype plots revealed superior performance for AutoDock Vina against WTMpro, while both FRED and AutoDock Vina demonstrated excellent performance for OMPro. In the second stage, VS was performed on a focused library of 636 compounds transformed from the early-enriched cluster related to perampanel via a scaffold hopping approach. Subsequently, molecular dynamics (MD) simulation and MM GBSA calculations validated the binding potential of the recommended hits against both explored targets. This study provides an example of how to conduct an in-depth benchmarking approach for both WTMPro and OMPro variants and offering an evaluated SBVS protocol for them both.