Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations

Abstract

Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of <i>R</i> _Dock = 0.72 ± 0.14 and <i>R</i> _W = -0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are <i>periandrin V</i>, <i>penimocycline</i>, <i>cis-p-Coumaroylcorosolic acid</i>, <i>glycyrrhizin</i>, and <i>uralsaponin B</i>. The obtained results could probably lead to enhance the COVID-19 therapy.