In silicoDrug Repurposing for COVID-19: Targeting SARS-CoV-2 Proteins through Docking and Consensus Ranking

被引:80
作者
Cavasotto, Claudio N. [1 ,2 ,3 ,4 ]
Di Filippo, Juan I. [1 ]
机构
[1] Univ Austral, CONICET, Translat Med Res Inst IIMT, Computat Drug Design & Biomed Informat Lab, Pilar, Buenos Aires, Argentina
[2] Univ Austral, Fac Ciencias Biomed, Pilar, Buenos Aires, Argentina
[3] Univ Austral, Fac Ingn, Pilar, Buenos Aires, Argentina
[4] Austral Inst Appl Artificial Intelligence, Pilar, Buenos Aires, Argentina
关键词
Molecular Docking; Consensus Scoring; Quantum Mechanical Scoring; COVID-19; SARS-CoV-2; Drug Repurposing; PAPAIN-LIKE PROTEASE; LIGAND DOCKING; NDDO APPROXIMATIONS; COUPLED RECEPTOR; OPTIMIZATION; FLEXIBILITY; PARAMETERS; DISCOVERY;
D O I
10.1002/minf.202000115
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
In December 2019, an infectious disease caused by the coronavirus SARS-CoV-2 appeared in Wuhan, China. This disease (COVID-19) spread rapidly worldwide, and on March 2020 was declared a pandemic by the World Health Organization (WHO). Today, over 21 million people have been infected, with more than 750.000 casualties. Today, no vaccine or antiviral drug is available. While the development of a vaccine might take at least a year, and for a novel drug, even longer; finding a new use to an old drug (drug repurposing) could be the most effective strategy. We present a docking-based screening using a quantum mechanical scoring of a library built from approved drugs and compounds undergoing clinical trials, against three SARS-CoV-2 target proteins: the spike or S-protein, and two proteases, the main protease and the papain-like protease. The S-protein binds directly to the Angiotensin Converting Enzyme 2 receptor of the human host cell surface, while the two proteases process viral polyproteins. Following the analysis of our structure-based compound screening, we propose several structurally diverse compounds (either FDA-approved or in clinical trials) that could display antiviral activity against SARS-CoV-2. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against the disease. We hope that these findings may contribute to the rational drug design against COVID-19.
引用
收藏
页数:8
相关论文
共 53 条
[1]   BIASED PROBABILITY MONTE-CARLO CONFORMATIONAL SEARCHES AND ELECTROSTATIC CALCULATIONS FOR PEPTIDES AND PROTEINS [J].
ABAGYAN, R ;
TOTROV, M .
JOURNAL OF MOLECULAR BIOLOGY, 1994, 235 (03) :983-1002
[2]   ICM - A NEW METHOD FOR PROTEIN MODELING AND DESIGN - APPLICATIONS TO DOCKING AND STRUCTURE PREDICTION FROM THE DISTORTED NATIVE CONFORMATION [J].
ABAGYAN, R ;
TOTROV, M ;
KUZNETSOV, D .
JOURNAL OF COMPUTATIONAL CHEMISTRY, 1994, 15 (05) :488-506
[3]   Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds [J].
Anh-Tien Ton ;
Gentile, Francesco ;
Hsing, Michael ;
Ban, Fuqiang ;
Cherkasov, Artem .
MOLECULAR INFORMATICS, 2020, 39 (08)
[4]  
Aucar MG, 2020, METHODS MOL BIOL, V2114, P269, DOI 10.1007/978-1-0716-0282-9_17
[5]   The SARS-coronavirus papain-like protease: Structure, function and inhibition by designed antiviral compounds [J].
Baez-Santos, Yahira M. ;
St John, Sarah E. ;
Mesecar, Andrew D. .
ANTIVIRAL RESEARCH, 2015, 115 :21-38
[6]   Docking and high throughput docking: Successes and the challenge of protein flexibility [J].
Cavasotto, Claudio N. ;
Singh, Narender .
CURRENT COMPUTER-AIDED DRUG DESIGN, 2008, 4 (03) :221-234
[7]   Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening [J].
Cavasotto, Claudio N. ;
Orry, Andrew J. W. ;
Murgolo, Nicholas J. ;
Czarniecki, Michael F. ;
Kocsi, Sue Ann ;
Hawes, Brian E. ;
O'Neill, Kim A. ;
Hine, Heather ;
Burton, Marybeth S. ;
Voigt, Johannes H. ;
Abagyan, Ruben A. ;
Bayne, Marvin L. ;
Monsma, Frederick J., Jr. .
JOURNAL OF MEDICINAL CHEMISTRY, 2008, 51 (03) :581-588
[8]   High-Throughput Docking Using Quantum Mechanical Scoring [J].
Cavasotto, Claudio N. ;
Aucar, M. Gabriela .
FRONTIERS IN CHEMISTRY, 2020, 8
[9]   Computational chemistry in drug lead discovery and design [J].
Cavasotto, Claudio N. ;
Gabriela Aucar, Maria ;
Adler, Natalia S. .
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, 2019, 119 (02)
[10]   Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization [J].
Cavasotto, Claudio N. ;
Adler, Natalia S. ;
Aucar, Maria G. .
FRONTIERS IN CHEMISTRY, 2018, 6