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| Item Type: | Article |
|---|---|
| Title: | AI-guided pipeline for protein-protein interaction drug discovery identifies an SARS-CoV-2 inhibitor |
| Creators: |
Trepte, P.  ORCID: https://orcid.org/0000-0002-8141-6272, Secker, C. ORCID: https://orcid.org/0000-0002-7222-536X, Olivet, J. ORCID: https://orcid.org/0000-0003-4115-5546, Blavier, J. ORCID: https://orcid.org/0000-0002-7055-6907, Kostova, S. ORCID: https://orcid.org/0000-0002-6842-6732, Maseko, S.B. ORCID: https://orcid.org/0000-0001-7025-6106, Minia, I. ORCID: https://orcid.org/0000-0003-1012-3373, Silva Ramos, E. ORCID: https://orcid.org/0000-0002-8520-9107, Cassonnet, P., Golusik, S., Zenkner, M., Beetz, S., Liebich, M.J., Scharek, N., Schütz, A. ORCID: https://orcid.org/0000-0002-0606-2574, Sperling, M. ORCID: https://orcid.org/0009-0003-9534-3398, Lisurek, M. ORCID: https://orcid.org/0009-0004-7685-5387, Wang, Y., Spirohn, K., Hao, T., Calderwood, M.A. ORCID: https://orcid.org/0000-0001-6475-1418, Hill, D.E. ORCID: https://orcid.org/0000-0001-5192-0921, Landthaler, M. ORCID: https://orcid.org/0000-0002-1075-8734, Choi, S.G. ORCID: https://orcid.org/0000-0003-0314-3958, Twizere, J.C. ORCID: https://orcid.org/0000-0002-8683-705X, Vidal, M. ORCID: https://orcid.org/0000-0003-3391-5410 and Wanker, E.E. ORCID: https://orcid.org/0000-0001-8072-1630
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| Abstract: | 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. |
| Keywords: | Protein-Protein Interactions, Machine Learning, AlphaFold, VirtualFlow, SARS-CoV-2 |
| Source: | Molecular Systems Biology |
| ISSN: | 1744-4292 |
| Publisher: | Willey |
| Volume: | 20 |
| Number: | 4 |
| Page Range: | 428-457 |
| Date: | 2 April 2024 |
| Official Publication: | https://doi.org/10.1038/s44320-024-00019-8 |
| PubMed: | View item in PubMed |
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