A reference map of the human binary protein interactome
Open accessAbstract
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.
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Of the functional human protein-protein interactions, only 50,000 (3%) have been identified and about 9,000 (0.5%) are structurally resolved.
“Of these, only 50,000 (3%) have been identified2, and ~9,000 (0.5%) are structurally resolved.”
From Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions (2025) · cites this paper as [2]
The Discussion reports a human interactome map with more than 50,000 identified PPIs, supporting the approximate count cited after “identified.” It does not establish that this is 3% of functional human PPIs; it says the cellular function of most individual PPIs remains to be elucidated and that estimating the interactome’s total size is challenging. The citation is attached to the identified-interactions figure, not the separate structurally resolved figure. (LLM, conf=0.86)
Experimental — accuracy not yet measuredExperimental approaches to discover protein-protein interactions at scale are laborious, yield many false positive and false negative interactions, and have not generated a comprehensive structural interactome.
“While these methods have uncovered many PPIs, they are laborious, yield many false positive and false negative interactions, and thus far have not generated a comprehensive structural interactome.”
From Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions (2025) · cites this paper as [2]
The Discussion (page unavailable) says that many PPIs remain undetected because the assays have limited sensitivity, and that HuRI remains incomplete. But the Generation and characterization of HuRI section reports low false-positive rates for its Y2H assays. The paper does not establish that these approaches are laborious or that they have failed to generate a comprehensive *structural* interactome. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCNP and ATP5PO may be functionally related or operate together to mediate phenotypic features associated with UDN-P9's disease and hypomyelinating leukodystropy-20.
“suggesting that they may be functionally related69–71 or operate together72,73 to mediate phenotypic features associated with UDN-P9’s disease and hypomyelinating leukodystropy-20”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [73]
The ‘Mechanisms of tissue-specific diseases’ section discusses how protein interactions may contribute to Mendelian disease phenotypes, but its examples involve other proteins. Searches for CNP, ATP5PO, UDN-P9, and hypomyelinating leukodystrophy found no passage in our copy addressing this pair or its proposed role in those phenotypes. (LLM, conf=0.70)
Experimental — accuracy not yet measuredCurrent estimates suggest that only about 1.5 million pairings among human proteins represent functional protein-protein interactions.
“current estimates suggest that only ~1.5 million pairings represent functional PPIs2”
From Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions (2025) · cites this paper as [2]
The Extended Data caption mentions estimates of the total binary protein interactome but gives no figure of 1.5 million. The Discussion says estimating its total size remains challenging; neither passage establishes how many human protein pairings are functional PPIs. Searches for “1.5 million” and “1,500,000” found no matching statement in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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“As shown in Figure S2E, 3.3%,19.4%, and 13.5% of the negative pairs (RefSet Random + RefSet Decoy ) exhibited “positive” pDockQ2, ipTM, and avg_models scores, respectively, even though at most ~0.75% of these random and decoy pairs should be positive 38 .”
InfluentialInterpretation of cancer mutations using a multiscale map of protein systems
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- Authors
- Luck, K., Kim, D.-K., Lambourne, L., Spirohn, K., Begg, B. E., Bian, W., Brignall, R., Cafarelli, T., Campos-Laborie, F. J., Charloteaux, B., Choi, D., Coté, A. G., Daley, M., Deimling, S., Desbuleux, A., Dricot, A., Gebbia, M., Hardy, M. F., Kishore, N., Knapp, J. J., Kovács, I. A., Lemmens, I., Mee, M. W., Mellor, J. C., Pollis, C., Pons, C., Richardson, A. D., Schlabach, S., Teeking, B., Yadav, A., Babor, M., Balcha, D., Basha, O., Bowman-Colin, C., Chin, S.-F., Choi, S. G., Colabella, C., Coppin, G., D’Amata, C., De Ridder, D., De Rouck, S., Duran-Frigola, M., Ennajdaoui, H., Goebels, F., Goehring, L., Gopal, A., Haddad, G., Hatchi, E., Helmy, M., Jacob, Y., Kassa, Y., Landini, S., Li, R., van Lieshout, N., MacWilliams, A., Markey, D., Paulson, J. N., Rangarajan, S., Rasla, J., Rayhan, A., Rolland, T., San-Miguel, A., Shen, Y., Sheykhkarimli, D., Sheynkman, G. M., Simonovsky, E., Taşan, M., Tejeda, A., Tropepe, V., Twizere, J.-C., Wang, Y., Weatheritt, R. J., Weile, J., Xia, Y., Yang, X., Yeger-Lotem, E., Zhong, Q., Aloy, P., Bader, G. D., De Las Rivas, J., Gaudet, S., Hao, T., Rak, J., Tavernier, J., Hill, D. E., Vidal, M., Roth, F. P., & Calderwood, M. A.
- Affiliations
- Harvard University (US)
- University of Toronto (CA)
- Consejo Superior de Investigaciones Científicas (ES)
- McGill University Health Centre (CA)
- Mount Sinai Hospital (CA)
- Vlaams Instituut voor Biotechnologie (BE)
- Institute for Research in Biomedicine (ES)
- Ben-Gurion University of the Negev (IL)
- University of Cambridge (GB)
- University of Perugia (IT)
- Centre National de la Recherche Scientifique (FR)
- University of Liège (BE)
- Dana-Farber Cancer Institute (US)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Universidad de Salamanca (ES)
- Canadian Institute for Advanced Research (CA)
- Corresponding author
- Hill, David E.
- Vidal, Marc
- Roth, Frederick P.
- Calderwood, Michael A.
- ORCID
- Luck, Katja
- Kim, Dae-Kyum
- Lambourne, Luke
- Spirohn, Kerstin
- Begg, Bridget E.
- Campos-Laborie, Francisco J.
- Choi, Dongsic
- Coté, Atina G.
- Desbuleux, Alice
- Gebbia, Marinella
- Kishore, Nishka
- Knapp, Jennifer J.
- Lemmens, Irma
- Mee, Miles W.
- Mellor, Joseph C.
- Pollis, Carl
- Pons, Carles
- Yadav, Anupama
- Basha, Omer
- Chin, Suet-Feung
- Choi, Soon Gang
- Colabella, Claudia
- Coppin, Georges
- Gopal, Anjali
- Haddad, Ghazal
- Hatchi, Elodie
- Helmy, Mohamed
- Jacob, Yves
- Paulson, Joseph N.
- Rolland, Thomas
- Sheykhkarimli, Dayag
- Sheynkman, Gloria M.
- Simonovsky, Eyal
- Tropepe, Vincent
- Twizere, Jean-Claude
- Wang, Yang
- Weatheritt, Robert J.
- Weile, Jochen
- Xia, Yu
- Yang, Xinping
- Yeger-Lotem, Esti
- Zhong, Quan
- Aloy, Patrick
- Bader, Gary D.
- De Las Rivas, Javier
- Gaudet, Suzanne
- Hao, Tong
- Rak, Janusz
- Hill, David E.
- Vidal, Marc
- Roth, Frederick P.
- Calderwood, Michael A.
- Venue
- Nature
- Published
- 2020-04-08
- Volume
- 580
- Issue
- 7803
- Pages
- 402-408
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC7169983
- OpenAlex
- W3015964336
- Cited by
- 1,197
- References
- 57
- Citations by year
- 2026: 133 · 2025: 176 · 2024: 228 · 2023: 201 · 2022: 202 · 2021: 190 · 2020: 64 · 2019: 1 · 2018: 1
- FWCI
- 54.83
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- ORBi (University of Liège) (submittedVersion, other-oa)
- HAL (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- Funders
- American Heart Association (15CVGPS23430000)
- Ukrainian Research Institute, Harvard University
- National Research Foundation
- Fonds De La Recherche Scientifique - FNRS
- Canada Excellence Research Chairs, Government of Canada
- National Institutes of Health (1u01hl098166-01, U01-HL098166)
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada
- National Heart, Lung, and Blood Institute (U01-HL098166)
- National Human Genome Research Institute (U01HG007690, U41HG001715, P50HG004233)
- National Cancer Institute (T32CA009361)
- National Institute of General Medical Sciences (R01GM109199)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Biotin and Related Studies
- Keywords
- monogenic disorders, genotype-phenotype correlation
- MeSH terms
- Extracellular Space, Humans, Organ Specificity, Proteome, Protein Interaction Mapping
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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