In Silico Identification of Potential Inhibitors of the Main Protease of SERS-CoV-2 Using Combined Ligand-based and Structure-based Drug Design approach

Abstract

Objectives: The outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) remains a serious global threat At the time of writing, there are no specific therapeutic agents or vaccines to combat this disease This study was designed to identify the SARS-CoV-2 main protease inhibitors using drug molecule information retrieved from DrugBank 5 0 (Wishart et al ) Methods: A set of common pharmacophores were generated from a series of 22 known SARS-CoV inhibitors The best pharmacophore used for virtual screening (VS) of DrugBank using the Phase module followed by structure-based virtual screening (VS) using Glide (Release 2020-1;Schrodinger LLC, New York, NY, USA) with SARS-CoV-2 main protease and 50 ns molecular dynamics (MD) simulation studies Results: Six hits were selected based on the fitness score, extra-precision Glide score, and binding affinity with the main protease (Mpro) The predicted inhibitor constant (Ki) values of the 3 best hits, DB03777, DB06834, and DB07456, were 0 8176, 0 2148, and 0 1006 mu M, respectively An MD simulation of DB07456 and DB13592 with the Mpro demonstrated stable protein-ligand complexes Conclusion: The selected inhibitors displayed a similar type of binding interaction with co-ligands and remdesivir, and the predicted Ki values of 2 inhibitors were found to be superior to remdesivir These selected hits may be used for further in vitro and in vivo studies against the SARS- CoV-2 Mpro