Architecture of the human interactome defines protein communities and disease networks

• Published in: Nature (London) Vol. 545; no. 7655; pp. 505 - 509
• Main Authors: Huttlin, Edward L., Bruckner, Raphael J., Paulo, Joao A., Cannon, Joe R., Ting, Lily, Baltier, Kurt, Colby, Greg, Gebreab, Fana, Gygi, Melanie P., Parzen, Hannah, Szpyt, John, Tam, Stanley, Zarraga, Gabriela, Pontano-Vaites, Laura, Swarup, Sharan, White, Anne E., Schweppe, Devin K., Rad, Ramin, Erickson, Brian K., Obar, Robert A., Guruharsha, K. G., Li, Kejie, Artavanis-Tsakonas, Spyros, Gygi, Steven P., Harper, J. Wade
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.05.2017; Nature Publishing Group
• Subjects: 631/1647/296; 631/61/475/2290; Cell Physiological Phenomena - genetics; Databases, Protein; Disease; Genome, Human; Humanities and Social Sciences; Humans; Interactomes; Intracellular Space - metabolism; letter; Markov Chains; Mass Spectrometry; Molecular Sequence Annotation; multidisciplinary; Open Reading Frames; Physiological aspects; Protein Interaction Mapping; Protein Interaction Maps; Proteome - analysis; Proteome - chemistry; Proteome - genetics; Proteome - metabolism; Science
• ISSN: 0028-0836; 1476-4687; 1476-4687
• DOI: 10.1038/nature22366

Affinity purification–mass spectrometry elucidates protein interaction networks and co-complexes to build, to our knowledge, the largest experimentally derived human protein interaction network so far, termed BioPlex 2.0. Mapping protein interactions The thousands of proteins within a cell function as modules and networks to coordinate their biological activities. Large-scale efforts are underway to build protein interaction maps that reveal cellular proteome architecture. Here, Wade Harper and colleagues use affinity purification mass spectrometry to elucidate protein interaction networks and co-complexes and build the largest experimentally derived human proteome interaction network to date, termed BioPlex 2.0. Containing over 29,000 novel co-associations and 1,300 protein communities representing diverse cellular activities, BioPlex 2.0 is more than double the size of their earlier interaction network BioPlex 1.0 and will be a valuable resource for exploring uncharacterized proteins and candidate disease-linked genes. 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 1 , 2 , 3 . Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification–mass spectrometry methodology 4 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 5 of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness 6 , 7 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.