AI-guided pipeline for protein-protein interaction drug discovery identifies an SARS-CoV-2 inhibitor


  • 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


  • Molecular Systems Biology


  • Mol Syst Biol 20 (4): 428-457


  • 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.