Identification of SARS-CoV-2 Main Protease Inhibitors Using Structure Based Virtual Screening and Molecular Dynamics Simulation of DrugBank Database.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is highly pathogenic to humans and has created an unprecedented global health care threat. Globally, intense efforts are going on to discover a vaccine or new drug molecules to control the COVID-19. However, till today, there is no effective therapeutics or treatment available for COVID-19. In this study, we aim to find out potential small molecule inhibitors for SARS-CoV-2 main protease (M) from the known DrugBank database version 5.1.8. We applied structure-based virtual screening of the database containing 11875 numbers of drug candidates to identify potential hits for SARS-CoV-2 M inhibitors. Seven potential inhibitors having admirable XP glide score ranging from -15.071 to -8.704 kcal/mol and good binding affinity with the active sites amino acids of M were identified. The selected hits were further analyzed with 50 ns molecular dynamics (MD) simulation to examine the stability of protein-ligand complexes. The root mean square deviation and potential energy plot indicates the stability of the complexes during the 50 ns MD simulation. The MM-GBSA analysis also showed good binding energy of the selected hits (-83.2718 to -58.6618 kcal/mol). Further analysis revealed critical hydrogen bonds and hydrophobic interactions between compounds and the target protein. The compounds bind to biologically important regions of M, indicating their potential to inhibit the functionality of this component.