AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor
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 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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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 15. Dez. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Trepte, Philipp [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.06.14.544560 |
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| PPN (Katalog-ID): |
XBI039898334 |
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| 520 | |a 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 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 |4 aut | |
| 700 | 1 | |a Maseko, Sibusiso B. |4 aut | |
| 700 | 1 | |a Choi, Soon Gang |4 aut | |
| 700 | 1 | |a Blavier, Jeremy |4 aut | |
| 700 | 1 | |a Minia, Igor |4 aut | |
| 700 | 1 | |a Ramos, Eduardo Silva |4 aut | |
| 700 | 1 | |a Cassonnet, Patricia |4 aut | |
| 700 | 1 | |a Golusik, Sabrina |4 aut | |
| 700 | 1 | |a Zenkner, Martina |4 aut | |
| 700 | 1 | |a Beetz, Stephanie |4 aut | |
| 700 | 1 | |a Liebich, Mara J. |4 aut | |
| 700 | 1 | |a Scharek, Nadine |4 aut | |
| 700 | 1 | |a Schütz, Anja |4 aut | |
| 700 | 1 | |a Sperling, Marcel |4 aut | |
| 700 | 1 | |a Lisurek, Michael |4 aut | |
| 700 | 1 | |a Wang, Yang |4 aut | |
| 700 | 1 | |a Spirohn, Kerstin |4 aut | |
| 700 | 1 | |a Hao, Tong |4 aut | |
| 700 | 1 | |a Calderwood, Michael A. |4 aut | |
| 700 | 1 | |a Hill, David E. |4 aut | |
| 700 | 1 | |a Landthaler, Markus |4 aut | |
| 700 | 1 | |a Olivet, Julien |4 aut | |
| 700 | 1 | |a Twizere, Jean-Claude |4 aut | |
| 700 | 1 | |a Vidal, Marc |4 aut | |
| 700 | 1 | |a Wanker, Erich E. |4 aut | |
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