Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease.

Eduardo Tejera,Yunierkis Pérez-Castillo,Andrés López-Cortés,Alejandro Cabrera-Andrade,Cristian R Munteanu

doi:10.3390/molecules25215172

Abstract

Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure–Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (Mpro) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the Mpro of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the Mpro enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.

Highlights

Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 [1], and it is the causative agent of Molecules 2020, 25, 5172; doi:10.3390/molecules25215172 www.mdpi.com/journal/moleculesMolecules 2020, 25, 5172 the novel human coronavirus disease 2019 (COVID-19) [2]
The outbreak of COVID-19 was declared as a Public Health Emergency of International Concern in January 2020, and as a pandemic in March 2020 by the World Health Organization (WHO) [3]
Quantitative Structure–Activity Relationship (QSAR) models are based on pre-calculated molecular features and they are obtained with traditional Machine Learning methods

Summary

Introduction

China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 [1], and it is the causative agent of Molecules 2020, 25, 5172; doi:10.3390/molecules25215172 www.mdpi.com/journal/molecules. Molecules 2020, 25, 5172 the novel human coronavirus disease 2019 (COVID-19) [2]. The family Coronavididae is made up of seven human coronaviruses that are primarily respiratory pathogens: OC43, 229E, KHU1, NL63, Middle East Respiratory Syndrome Coronavirus (MERS-CoV), SARS-CoV, and SARS-CoV-2 [5]. The last three are members of the genus Betacoronavirus, which are characterized by causing mild to severe respiratory diseases, and having high mutation rates that result in viral genetic diversity, plasticity, and adaptability to invade a wide range of hosts [6]. The first SARS-CoV-2 genome (Wuhan-Hu-1; NC_045512) was sequenced in China in January

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