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

Mol Syst Biol. 2024 Apr;20(4):428-457. doi: 10.1038/s44320-024-00019-8. Epub 2024 Mar 11.

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.

Keywords: AlphaFold; Machine Learning; Protein–Protein Interactions; SARS-CoV-2; VirtualFlow.

MeSH terms

  • Artificial Intelligence
  • COVID-19*
  • Drug Discovery
  • Humans
  • Methyltransferases / metabolism
  • SARS-CoV-2* / metabolism

Substances

  • Methyltransferases