Towards a structurally resolved human protein interaction network.
Nat Struct Mol Biol
; 30(2): 216-225, 2023 02.
Article
in En
| MEDLINE
| ID: mdl-36690744
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Signal Transduction
/
Protein Interaction Maps
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Nat Struct Mol Biol
Journal subject:
BIOLOGIA MOLECULAR
Year:
2023
Document type:
Article
Affiliation country:
United kingdom