AI-guided pipeline for protein-protein interaction drug discovery identifies an SARS-CoV-2 inhibitor
Authors
- P. Trepte
- C. Secker
- J. Olivet
- J. Blavier
- S. Kostova
- S.B. Maseko
- I. Minia
- E. Silva Ramos
- P. Cassonnet
- S. Golusik
- M. Zenkner
- S. Beetz
- M.J. Liebich
- N. Scharek
- A. Schutz
- M. Sperling
- M. Lisurek
- Y. Wang
- K. Spirohn
- T. Hao
- M.A. Calderwood
- D.E. Hill
- M. Landthaler
- S.G. Choi
- J.C. Twizere
- M. Vidal
- E.E. Wanker
Journal
- Molecular Systems Biology
Citation
- Mol Syst Biol 20 (4): 428-457
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.