AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor
© 2024. The Author(s)..
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.
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:20 |
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| Enthalten in: |
Molecular systems biology - 20(2024), 4 vom: 01. Apr., Seite 428-457 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Trepte, Philipp [VerfasserIn] |
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| Links: |
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| Themen: |
AlphaFold |
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| Anmerkungen: |
Date Completed 04.04.2024 Date Revised 05.04.2024 published: Print-Electronic UpdateOf: bioRxiv. 2023 Jun 14;:. - PMID 37398436 Citation Status MEDLINE |
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| doi: |
10.1038/s44320-024-00019-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM369575644 |
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| 520 | |a © 2024. The Author(s). | ||
| 520 | |a 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 | ||
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| 700 | 1 | |a Kostova, Simona |e verfasserin |4 aut | |
| 700 | 1 | |a Maseko, Sibusiso B |e verfasserin |4 aut | |
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| 700 | 1 | |a Cassonnet, Patricia |e verfasserin |4 aut | |
| 700 | 1 | |a Golusik, Sabrina |e verfasserin |4 aut | |
| 700 | 1 | |a Zenkner, Martina |e verfasserin |4 aut | |
| 700 | 1 | |a Beetz, Stephanie |e verfasserin |4 aut | |
| 700 | 1 | |a Liebich, Mara J |e verfasserin |4 aut | |
| 700 | 1 | |a Scharek, Nadine |e verfasserin |4 aut | |
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| 700 | 1 | |a Spirohn, Kerstin |e verfasserin |4 aut | |
| 700 | 1 | |a Hao, Tong |e verfasserin |4 aut | |
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