AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor.
AlphaFold
Machine Learning
Protein–Protein Interactions
SARS-CoV-2
VirtualFlow
Journal
Molecular systems biology
ISSN: 1744-4292
Titre abrégé: Mol Syst Biol
Pays: England
ID NLM: 101235389
Informations de publication
Date de publication:
11 Mar 2024
11 Mar 2024
Historique:
received:
09
02
2023
accepted:
23
01
2024
revised:
22
01
2024
medline:
12
3
2024
pubmed:
12
3
2024
entrez:
12
3
2024
Statut:
aheadofprint
Résumé
Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.
Identifiants
pubmed: 38467836
doi: 10.1038/s44320-024-00019-8
pii: 10.1038/s44320-024-00019-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Bundesministerium für Bildung und Forschung (BMBF)
ID : 01GS0844
Organisme : Deutsche Forschungsgemeinschaft (DFG)
ID : LA 2941/17-1
Organisme : Fonds De La Recherche Scientifique - FNRS (FNRS)
ID : FC27371
Organisme : Fonds De La Recherche Scientifique - FNRS (FNRS)
ID : FC38907
Organisme : Fonds De La Recherche Scientifique - FNRS (FNRS)
ID : FC34947
Organisme : Fonds De La Recherche Scientifique - FNRS (FNRS)
ID : FC34947
Organisme : Wallonia-Brussels International (WBI)-World Excellence Fellowship
ID : 40003579
Organisme : National Institute of Health (NIH)
ID : P50HG004233
Organisme : National Institute of Health (NIH)
ID : R01GM130885
Organisme : National Institute of Health (NIH)
ID : U41HG001715
Organisme : National Institute of Health (NIH)
ID : R01CA266194
Organisme : LabEx IBEID
ID : 10-LABX-0062
Informations de copyright
© 2024. The Author(s).
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