A reference map of the human binary protein interactome

By Katja Luck, Dae-Kyum Kim, L. Lambourne, Kerstin Spirohn, Bridget E. Begg, Wenting Bian, Ruth Brignall, T. Cafarelli, F. Campos-Laborie, B. Charloteaux and 78 more. 2020

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Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype–phenotype relationships1,2. Here we present a human ‘all-by-all’ reference interactome map of human binary protein interactions, or ‘HuRI’. With approximately 53,000 protein–protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein–protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes. A human binary protein interactome map that includes around 53,000 protein–protein interactions involving more than 8,000 proteins provides a reference for the study of human cellular function in health and disease.

Subfields: Molecular Biology, Spectroscopy

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Citations in the corpus

Graph Representation Learning in Biomedicine and Healthcare (2021, neutral): “Owing to experimental and resource constraints, the most updated networks of protein–protein interactions are limited in their number of nodes (proteins) and edges (physical interactions) 115 .”
Towards a structurally resolved human protein interaction network (2021, background): “The N-terminal domain in HNRNPC (16-85) is predicted to interact with the N-terminal domain of RALY (1-100).”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “This is an ongoing effort and we retrieved the third phase release of the project (HuRI (Luck et al. 2020)), which contains 51,813 positive PPI pairs from 8,248 proteins.”
Network medicine framework for identifying drug-repurposing opportunities for COVID-19 (2020, methodology): “ the number of successive operations of convolutional layers in the neural network model. The graph convolutional operator takes the form 8 5 # ()*+)=6 #( ∑ 8 - #$#TAB#9 ()5 $ )+8 -5 # )) - $∈/! " , (1) where 5 # ())∈-%() is the hidden &state of node $ in the ;’ layer of the neural network ”
OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization (2022, methodology): “…we compared its precision and recall to Bioplex [overexpression of hemagglutinin-tagged baits ( 8 , 32 )], the yeast two-hybrid human reference interactome [HuRI ( 6 )], and our own previous data [green fluorescent protein fusions expressed from bacterial artificial chromosomes ( 7 )] (fig.”
Building a knowledge graph to enable precision medicine (2022, methodology): “We also consider the human reference interac-tome (HuRI) generated by Luck et al. 88 , speciﬁcally, we use the HI-union, a combination of HuRI and several related efforts to systematically screen for protein-protein interactions.”