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dc.contributor.authorBurke, David F.
dc.contributor.authorBryant, Patrick
dc.contributor.authorBarrio-Hernandez, Inigo
dc.contributor.authorMemon, Danish
dc.contributor.authorPozzati, Gabriele
dc.contributor.authorShenoy, Aditi
dc.contributor.authorZhu, Wensi
dc.contributor.authorDunham, Alistair S.
dc.contributor.authorAlbanese, Pascal
dc.contributor.authorKeller, Andrew
dc.contributor.authorScheltema, Richard A.
dc.contributor.authorBruce, James E.
dc.contributor.authorLeitner, Alexander
dc.contributor.authorKundrotas, Petras
dc.contributor.authorBeltrao, Pedro
dc.contributor.authorElofsson, Arne
dc.date.accessioned2023-06-13T16:58:27Z
dc.date.available2023-06-13T16:58:27Z
dc.date.issued2023-01-23
dc.identifier.citationBurke, D.F., Bryant, P., Barrio-Hernandez, I. et al. Towards a structurally resolved human protein interaction network. Nat Struct Mol Biol 30, 216–225 (2023). https://doi.org/10.1038/s41594-022-00910-8en_US
dc.identifier.urihttps://hdl.handle.net/1808/34357
dc.description.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.en_US
dc.publisherNature Researchen_US
dc.rightsCopyright © 2023, The Author(s). This is an open access article distributed under the terms of the Creative Commons CC BY license.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectProtein foldingen_US
dc.subjectStructural biologyen_US
dc.subjectSystems biologyen_US
dc.titleTowards a structurally resolved human protein interaction networken_US
dc.typeArticleen_US
kusw.kuauthorKundrotas, Petras
kusw.kudepartmentCenter for Computational Biologyen_US
dc.identifier.doi10.1038/s41594-022-00910-8en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-8830-3951en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-3439-1866en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-5686-0451en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1365-0710en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-4303-9939en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-7748-2501en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-9076-3025en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1668-0253en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-6441-6089en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-4126-0725en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-5080-1664en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-2724-7703en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-7115-9751en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.identifier.pmidPMC9935395en_US
dc.rights.accessrightsopenAccessen_US


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Copyright © 2023, The Author(s). This is an open access article distributed under the terms of the Creative Commons CC BY license.
Except where otherwise noted, this item's license is described as: Copyright © 2023, The Author(s). This is an open access article distributed under the terms of the Creative Commons CC BY license.