@article{TrepteSeckerOlivetetal.2024, author = {Trepte, Philipp and Secker, Christopher and Olivet, Julien and Blavier, Jeremy and Kostova, Simona and Maseko, Sibusiso B and Minia, Igor and Silva Ramos, Eduardo and Cassonnet, Patricia and Golusik, Sabrina and Zenkner, Martina and Beetz, Stephanie and Liebich, Mara J and Scharek, Nadine and Sch{\"u}tz, Anja and Sperling, Marcel and Lisurek, Michael and Wang, Yang and Spirohn, Kerstin and Hao, Tong and Calderwood, Michael A and Hill, David E and Landthaler, Markus and Choi, Soon Gang and Twizere, Jean-Claude and Vidal, Marc and Wanker, Erich E}, title = {AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor}, volume = {20}, journal = {Molecular Systems Biology}, number = {4}, publisher = {Springer Science and Business Media LLC}, issn = {1744-4292}, doi = {https://doi.org/10.1038/s44320-024-00019-8}, pages = {428 -- 457}, year = {2024}, 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.}, language = {en} }