@article{TrepteSeckerKostovaetal., author = {Trepte, Philipp and Secker, Christopher and Kostova, Simona and Maseko, Sibusiso B. and Gang Choi, Soon and Blavier, Jeremy 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 Olivet, Julien 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}, series = {bioRxiv}, journal = {bioRxiv}, doi = {10.1101/2023.06.14.544560}, language = {en} } @article{TrepteSeckerOlivetetal., 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}, series = {Molecular Systems Biology}, 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}, 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} }