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Probing antiviral drugs against SARS-CoV-2 through virus-drug association prediction based on the KATZ method.
- Source :
-
Genomics [Genomics] 2020 Nov; Vol. 112 (6), pp. 4427-4434. Date of Electronic Publication: 2020 Jul 31. - Publication Year :
- 2020
-
Abstract
- It is urgent to find an effective antiviral drug against SARS-CoV-2. In this study, 96 virus-drug associations (VDAs) from 12 viruses including SARS-CoV-2 and similar viruses and 78 small molecules are selected. Complete genomic sequence similarity of viruses and chemical structure similarity of drugs are then computed. A KATZ-based VDA prediction method (VDA-KATZ) is developed to infer possible drugs associated with SARS-CoV-2. VDA-KATZ obtained the best AUCs of 0.8803 when the walking length is 2. The predicted top 3 antiviral drugs against SARS-CoV-2 are remdesivir, oseltamivir, and zanamivir. Molecular docking is conducted between the predicted top 10 drugs and the virus spike protein/human ACE2. The results showed that the above 3 chemical agents have higher molecular binding energies with ACE2. For the first time, we found that zidovudine may be effective clues of treatment of COVID-19. We hope that our predicted drugs could help to prevent the spreading of COVID.<br /> (Copyright © 2020. Published by Elsevier Inc.)
- Subjects :
- Adenosine Monophosphate analogs & derivatives
Adenosine Monophosphate metabolism
Adenosine Monophosphate pharmacology
Alanine analogs & derivatives
Alanine metabolism
Alanine pharmacology
Angiotensin-Converting Enzyme 2 chemistry
Angiotensin-Converting Enzyme 2 metabolism
Antiviral Agents chemistry
Host-Pathogen Interactions drug effects
Humans
Oseltamivir metabolism
Oseltamivir pharmacology
Spike Glycoprotein, Coronavirus chemistry
Spike Glycoprotein, Coronavirus metabolism
Zanamivir metabolism
Zanamivir pharmacology
Antiviral Agents metabolism
Antiviral Agents pharmacology
Drug Evaluation, Preclinical methods
Molecular Docking Simulation methods
SARS-CoV-2 drug effects
Subjects
Details
- Language :
- English
- ISSN :
- 1089-8646
- Volume :
- 112
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- Genomics
- Publication Type :
- Academic Journal
- Accession number :
- 32745502
- Full Text :
- https://doi.org/10.1016/j.ygeno.2020.07.044