Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs
Maryam Hosseini, Wanqiu Chen, Daliao Xiao, Charles Wang
Precision Clinical Medicine, doi:10.1093/pcmedi/pbab001
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 23 November 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-structural proteins (NSP). Three non-structural proteins, main protease (Mpro), papain-like protease (PLpro), and RNAdependent RNA polymerase (RdRp), are believed to have a crucial role in replication of the virus. We applied computational ligand-receptor binding modeling and performed comprehensive virtual screening on 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 results 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.
Supplementary data Supplementary data are available at PCMEDI online.
Conflict of interest statement All authors declared no conflict of interests. In addition, as an Editorial Board Member of Precision Clinical Medicine, the corresponding author Charles Wang was blinded from reviewing and making decision on this manuscript.
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'abstract': '<jats:title>Abstract</jats:title><jats:p>The pandemic of novel coronavirus disease 2019 '
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