In Silico Identification of Potential Inhibitors of the Main Protease of SARS-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; Schrödinger 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 μM, 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.