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protein, pubmed, interactions, article, predicted, complex, fig, models, interface, data, proteins, pairs, google, scholar, human, mutations, pdockq, complexes, cas, structures, structural, nature, central, interaction, model, residues, supplementary, structure, phosphosites, huri, experimental, humap, alphafold, interfaces, phosphorylation, large, source, biology, confidence, predictions, molecular, score, subunits, information, show, prediction, cancer, methods, build, set,

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nature portfolio privacy policy graphical editing imac-enrichable cross-linking reagent advertising proteome-wide cross-link identifications dutch research council correct solutions social media middle circle biomolecular research molecular systems biology reprints cross-linking mass spectrometry development research design applied research nature 580 nature 490 nature 596 nature chaperonin tric/cct associates ints10-ints13-ints14 form robust proteome-wide approaches sided hyper-geometric tests dna double-strand breaks full size image open conformation sided hyper-geometric test hereditary cancer-predisposing syndrome peer reviewer reports protein–protein association networks peer review original author interaction features impacting sided z-test comparing gene ontology terms permissions c-terminal residues k275 adding structural details large-scale structural analysis tools human cell biology single-chain hhblits runs large-scale ppi studies structurally resolved interactome build higher-order assemblies neural network-based approaches complex structural features previous structural information

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         headline:Towards a structurally resolved human protein interaction network
         description: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.
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      headline:Towards a structurally resolved human protein interaction network
      description: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.
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               name:Science for Life Laboratory, Stockholm University, Solna, Sweden
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            address:
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      name:Science for Life Laboratory, Stockholm University, Solna, Sweden
      name:Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
      name:European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
      name:European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
      name:Science for Life Laboratory, Stockholm University, Solna, Sweden
      name:Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
      name:Science for Life Laboratory, Stockholm University, Solna, Sweden
      name:Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
      name:Science for Life Laboratory, Stockholm University, Solna, Sweden
      name:Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
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      name:Science for Life Laboratory, Stockholm University, Solna, Sweden
      name:Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden

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