Target identification for repurposed drugs active against SARS-CoV-2 via high-throughput inverse docking.
J Comput Aided Mol Des
; 36(1): 25-37, 2022 01.
Article
in English
| MEDLINE | ID: covidwho-1536333
ABSTRACT
Screening already approved drugs for activity against a novel pathogen can be an important part of global rapid-response strategies in pandemics. Such high-throughput repurposing screens have already identified several existing drugs with potential to combat SARS-CoV-2. However, moving these hits forward for possible development into drugs specifically against this pathogen requires unambiguous identification of their corresponding targets, something the high-throughput screens are not typically designed to reveal. We present here a new computational inverse-docking protocol that uses all-atom protein structures and a combination of docking methods to rank-order targets for each of several existing drugs for which a plurality of recent high-throughput screens detected anti-SARS-CoV-2 activity. We demonstrate validation of this method with known drug-target pairs, including both non-antiviral and antiviral compounds. We subjected 152 distinct drugs potentially suitable for repurposing to the inverse docking procedure. The most common preferential targets were the human enzymes TMPRSS2 and PIKfyve, followed by the viral enzymes Helicase and PLpro. All compounds that selected TMPRSS2 are known serine protease inhibitors, and those that selected PIKfyve are known tyrosine kinase inhibitors. Detailed structural analysis of the docking poses revealed important insights into why these selections arose, and could potentially lead to more rational design of new drugs against these targets.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Antiviral Agents
/
Protease Inhibitors
/
Serine Endopeptidases
/
Pharmaceutical Preparations
/
Drug Repositioning
/
SARS-CoV-2
/
COVID-19 Drug Treatment
Type of study:
Diagnostic study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
J Comput Aided Mol Des
Journal subject:
Molecular Biology
/
Biomedical Engineering
Year:
2022
Document Type:
Article
Affiliation country:
S10822-021-00432-3
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