Fast Identification of Possible Drug Treatment of Coronavirus Disease −19 (COVID-19) Through Computational Drug Repurposing Study

By Junmei Wang. Journal of Chemical Information and Modeling, 2020

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The recent outbreak of novel coronavirus disease −19 (COVID-19) calls for and welcomes possible treatment strategies using drugs on the market. It is very efficient to apply computer-aided drug design techniques to quickly identify promising drug repurposing candidates, especially after the detailed 3D-structures of key virous proteins are resolved. The virus causing COVID-19 is SARS-Cov-2. Taking the advantage of a recently released crystal structure of SARS-Cov-2 main protease in complex with a covalently-bonded inhibitor, N3,1 I conducted virtual docking screening of approved drugs and drug candidates in clinical trials. For the top docking hits, I then performed molecular dynamics simulations followed by binding free energy calculations using an endpoint method called MM-PBSA-WSAS (Molecular Mechanics-Poisson Boltzmann Surface Area-Weighted Solvent-Accessible Surface Area).2-4 Several promising known drugs stand out as potential inhibitors of SARS-Cov-2 main protease, including Carfilzomib, Eravacycline, Valrubicin, Lopinavir and Elbasvir. Carfilzomib, an approved anti-cancer drug acting as a proteasome inhibitor, has the best MM-PBSA-WSAS binding free energy, −13.8 kcal/mol. The second-best repurposing drug candidate, eravacycline, is synthetic halogenated tetracycline class antibiotic. Streptomycin, another antibiotic and a charged molecule, also demonstrates some inhibitory effect, even though the predicted binding free energy of the charged form (−3.8 kcal/mol) is not nearly as low as that of the neutral form (−7.9 kcal/mol). One bioactive, PubChem 23727975, has a binding free energy of −12.9 kcal/mol. Detailed receptor-ligand interactions were analyzed and hot spots for the receptor-ligand binding were identified. I found that one hotspot residue HIS41, is a conserved residue across many viruses including SARS-Cov, SARS-Cov-2, MERS-Cov, and HCV. The findings of this study can facilitate rational drug design targeting the SARS-Cov-2 main protease.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5d466ddd8d5f31b7a864251ec5f7540e90fb0289