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 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 and 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: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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| Enthalten in: |
bioRxiv : the preprint server for biology - (2023) vom: 14. Juni |
| 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 Revised 02.04.2024 published: Electronic UpdateIn: Mol Syst Biol. 2024 Mar 11;:. - PMID 38467836 Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1101/2023.06.14.544560 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM358996406 |
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| 500 | |a UpdateIn: Mol Syst Biol. 2024 Mar 11;:. - PMID 38467836 | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 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 and 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 | |
| 700 | 1 | |a Choi, Soon Gang |e verfasserin |4 aut | |
| 700 | 1 | |a Blavier, Jeremy |e verfasserin |4 aut | |
| 700 | 1 | |a Minia, Igor |e verfasserin |4 aut | |
| 700 | 1 | |a Ramos, Eduardo Silva |e verfasserin |4 aut | |
| 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 | |
| 700 | 1 | |a Schütz, Anja |e verfasserin |4 aut | |
| 700 | 1 | |a Sperling, Marcel |e verfasserin |4 aut | |
| 700 | 1 | |a Lisurek, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Spirohn, Kerstin |e verfasserin |4 aut | |
| 700 | 1 | |a Hao, Tong |e verfasserin |4 aut | |
| 700 | 1 | |a Calderwood, Michael A |e verfasserin |4 aut | |
| 700 | 1 | |a Hill, David E |e verfasserin |4 aut | |
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