Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 M(pro) Protease
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Identificadores
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Fecha de publicación
2020Título de revista
MOLECULES
Tipo de contenido
Journal Article
DeCS
amobarbital | unión proteica | programas informáticos | antivíricos | simulación de acoplamiento molecular | pandemias | bibliotecas de moléculas pequeñas | sitios de unión | simulación de dinámicas moleculares | nuevas indicaciones de medicamentos | tiroxina | relación cuantitativa estructura-actividad | humanos | inhibidores de proteasas | simulación por ordenador | descubrimiento de fármacos | termodinámicaMeSH
Pandemics | Drug Repositioning | Small Molecule Libraries | Protease Inhibitors | Binding Sites | Protein Binding | Thyroxine | Quantitative Structure-Activity Relationship | Thermodynamics | Humans | Drug Discovery | Amobarbital | Molecular Docking Simulation | Molecular Dynamics Simulation | Computer Simulation | Software | Antiviral AgentsResumen
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 (M(pro)) 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 M(pro) 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 M(pro) enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.