Towards a structurally resolved human protein interaction network

• DOI: 10.1038/s41594-022-00910-8
• Publication Date: 2023-01-23T17:02:44Z
• Authors: David F. Burke; Patrick Bryant; Inigo Barrio-Hern...; Danish Memon; Gabriele Pozzati; Aditi Shenoy; Wensi Zhu; Alistair S. Dunham; Pascal Albanese; Andrew Keller; Richard A. Scheltema; James E. Bruce; Alexander Leitner; Petras Kundrotas; Pedro Beltrao; Arne Elofsson
• Keywords: 0301 basic medicine; 570; 0303 health sciences; 500; Computational Biology; Computational Biology/methods; Article; 03 medical and health sciences; SDG 3 - Good Health and Well-being; Mutation; Humans; Protein folding; Protein Interaction Maps; Structural biology; Systems biology; Signal Transduction

AbstractCellular functions are governed by molecular machines that assemble through protein-protein interactions. Their atomic details are critical to studying their molecular mechanisms. However, fewer than 5% of hundreds of thousands of human protein interactions have been structurally characterized. Here we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure. We identify interface residues harboring disease mutations, suggesting potential mechanisms for pathogenic variants. Groups of interface phosphorylation sites show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple protein interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies helping to expand our understanding of human cell biology.