AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-CoV-2 inhibitor
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...
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Veröffentlicht in: | Molecular systems biology 2024-04, Vol.20 (4), p.428-457 |
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Sprache: | eng |
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Zusammenfassung: | 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.
Synopsis
A new pipeline for prioritizing protein-protein interactions (PPIs) for drug discovery, combines machine learning-based scoring of quantitative PPI data, protein complex structure prediction and virtual drug screening.
A multi-adaptive support vector machine (maSVM) classifier is used for scoring PPIs from quantitative interaction and structure prediction data.
The machine learning-based classifier is applicable to PPI datasets from various assays and AlphaFold-Multimer predictions improving comparability between different methods.
Interaction mapping with LuTHy and maSVM-based scoring identified high-confidence SARS-CoV-2 PPIs. Subsequent AlphaFold-Multimer predictions revealed key interaction residues within the NSP10-NSP16 methyltransferase complex.
Targeting the complex with virtual compound screening identified an early-stage small molecule inhibitor that disrupts the NSP10-NSP16 interaction and SARS-CoV-2 replication.
A new pipeline for prioritizing protein-protein interactions (PPIs) for drug discovery, combines machine learning-based scoring of quantitative PPI data, protein complex structure prediction and virtual drug screening. |
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ISSN: | 1744-4292 1744-4292 |
DOI: | 10.1038/s44320-024-00019-8 |