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
Hauptverfasser: Trepte, Philipp, Secker, Christopher, Olivet, Julien, Blavier, Jeremy, Kostova, Simona, Maseko, Sibusiso B, Minia, Igor, Silva Ramos, Eduardo, Cassonnet, Patricia, Golusik, Sabrina, Zenkner, Martina, Beetz, Stephanie, Liebich, Mara J, Scharek, Nadine, Schütz, Anja, Sperling, Marcel, Lisurek, Michael, Wang, Yang, Spirohn, Kerstin, Hao, Tong, Calderwood, Michael A, Hill, David E, Landthaler, Markus, Choi, Soon Gang, Twizere, Jean-Claude, Vidal, Marc, Wanker, Erich E
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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.
ISSN:1744-4292
1744-4292
DOI:10.1038/s44320-024-00019-8