Trepte P, Secker C, Olivet J, Blavier J, Kostova S, Maseko SB, Minia I, Silva Ramos E, Cassonnet P, Golusik S, Zenkner M, Beetz S, Liebich MJ, Scharek N, Schuetz A, Sperling M, Lisurek M, Wang Y, Spirohn K, Hao T, Calderwood MA, Hill DE, Landthaler M, Choi SG, Twizere JC, Vidal M, Wanker EE

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 ...

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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.

Date: Apr. 01, 2024

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