Architecture of the human interactome defines protein communities and disease networks

By Edward L. Huttlin, Raphael J. Bruckner, J. Paulo, Joe R. Cannon, Lily Ting, Kurt Baltier, G. Colby, Fana Gebreab, Melanie P. Gygi, Hannah Parzen and 15 more. 2017

1,317 citations recorded by the source corpus.

The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein–protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidating how genome variation contributes to disease. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification–mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.

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Citations in the corpus

Evolution of resilience in protein interactomes across the tree of life (2018, background): “The removal of even a small number of proteins can completely fragment the interactome and lead to cell death and disease (4, 5) (SI Appendix, section S5.”
PIP4Ks Suppress Insulin Signaling through a Catalytic-Independent Mechanism (2018, background): “Additionally, unbiased, highthroughput mass spectrometry has revealed evidence of interaction between PIP5Ka and PIP4Ka (Huttlin et al., 2017).”
A reference map of the human binary protein interactome (2020, background): “Indeed, contrary to protein complex 11–13 and literature-curated interactome maps 38 as well as our previously published binary PPI datasets 2,14 , we find that TiP proteins are well-represented in HuRI (Fig.”
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OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization (2022, methodology): “S1C; about one-tenth of the material required in previous approaches (7, 8)].”
Quantitative Proteomics of the Cancer Cell Line Encyclopedia (2020, methodology): “0 network for both direct links and reported community memberships (Huttlin et al., 2017).”
A Human IgSF Cell-Surface Interactome Reveals a Complex Network of Protein-Protein Interactions (2020, background): “Mapping extracellular PPIs has proved challenging because most cell-surface proteins are refractory to classic biochemical screening techniques and cell-surface PPIs are underrepresented in affinity purification/mass spectrometry-based datasets (Huttlin et al., 2015, 2017).”
Rapid determination of quaternary protein structures in complex biological samples (2019, methodology): “(1) In DDA-MS, we search the MS data for all theoretical m/z values for fragments of cross-linked peptides across fragment-ion spectra (MS2) and rank by cross-linked peptide sequence coverage.”
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