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Issue 53, 2020
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Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations

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Abstract

Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of RDock = 0.72 ± 0.14 and RW = −0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are periandrin V, penimocycline, cis-p-Coumaroylcorosolic acid, glycyrrhizin, and uralsaponin B. The obtained results could probably lead to enhance the COVID-19 therapy.

Graphical abstract: Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations

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Supplementary files

Article information


Submitted
16 Jul 2020
Accepted
10 Aug 2020
First published
28 Aug 2020

This article is Open Access

RSC Adv., 2020,10, 31991-31996
Article type
Paper

Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations

M. Q. Pham, K. B. Vu, T. N. Han Pham, L. T. Thuy Huong, L. H. Tran, N. T. Tung, V. V. Vu, T. H. Nguyen and S. T. Ngo, RSC Adv., 2020, 10, 31991
DOI: 10.1039/D0RA06212J

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