Towards a structurally resolved human protein interaction network

Cellular 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. Here the authors explore the ability of AlphaFold2 to predict structures across the human protein-protein interactome and the limitations thereof.

Automated summary

In this study, the authors investigate the use of AlphaFold2 to predict protein structures in the human protein-protein interactome and discuss its limitations. They demonstrate the high confidence of the predicted models and identify potential mechanisms for disease mutations. Additionally, they show the application of predicted binary complexes in expanding our understanding of human cell biology.