Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs

Hosseini, Maryam; Chen, Wanqiu; Xiao, Daliao; Wang, Charles

The pandemic of novel coronavirus disease 2019 (COVID-19) has rampaged the world with more than 58.4 million confirmed cases and over 1.38 million deaths across the world by November 23, 2020. There is an urgent need to identify effective drugs and vaccines to fight against the virus. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the family of coronaviruses consisting of four structural and 16 non-structured proteins. Three non-structural proteins such as main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp) are believed to play a crucial role in the virus replication. We applied a computational ligand-receptor binding modeling and performed a comprehensive virtual screening on the FDA-approved drugs against these three SARS-CoV-2 proteins using AutoDock Vina, Glide, and rDock. Our computational studies identified six novel ligands as potential inhibitors against SARS-CoV-2, including antiemetics Rolapitant and Ondansetron for Mpro; Labetalol and Levomefolic acid for PLpro; and Leucal and antifungal Natamycin for RdRp. Molecular dynamics simulation confirmed the stability of the ligand-protein complexes. The result of our analysis with some other suggested drugs indicated that chloroquine and hydroxychloroquine had high binding energy (low inhibitory effect) with all three proteins—Mpro, PLpro, and RdRp. In summary, our computational molecular docking approach and virtual screening identified some promising candidate SARS-CoV-2 inhibitors that may be considered for further clinical studies.