hu.MAP3.0: atlas of human protein complexes by integration of >25,000 proteomic experiments
Open accessPublished version of a preprint
Abstract
Macromolecular protein complexes carry out most cellular functions. Unfortunately, we lack the subunit composition for many human protein complexes. To address this gap we integrated >25,000 mass spectrometry experiments using a machine learning approach to identify >15,000 human protein complexes. We show our map of protein complexes is highly accurate and more comprehensive than previous maps, placing nearly 70% of human proteins into their physical contexts. We globally characterize our complexes using mass spectrometry based protein covariation data (ProteomeHD.2) and identify covarying complexes suggesting common functional associations. hu.MAP3.0 generates testable functional hypotheses for 472 uncharacterized proteins which we support using AlphaFold modeling. Additionally, we use AlphaFold modeling to identify 5871 mutually exclusive proteins in hu.MAP3.0 complexes suggesting complexes serve different functional roles depending on their subunit composition. We identify expression as the primary way cells and organisms relieve the conflict of mutually exclusive subunits. Finally, we import our complexes to EMBL-EBI’s Complex Portal ( https://www.ebi.ac.uk/complexportal/home ) and provide complexes through our hu.MAP3.0 web interface ( https://humap3.proteincomplexes.org/ ). We expect our resource to be highly impactful to the broader research community.
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References 124 · Cited by 25
- Authors
- Fischer, S. N., Claussen, E. R., Kourtis, S., Sdelci, S., Orchard, S., Hermjakob, H., Kustatscher, G., & Drew, K.
- Affiliations
- University of Illinois Chicago (US)
- Barcelona Institute of Science and Technology (ES)
- European Bioinformatics Institute (GB)
- Wellcome Centre for Cell Biology (GB)
- Corresponding author
- Fischer, Samantha N
- Claussen, Erin R
- ORCID
- Venue
- Molecular Systems Biology
- Published
- 2025-05-27
- Volume
- 21
- Issue
- 7
- Pages
- 911-943
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC12222714
- OpenAlex
- W4410773163
- Cited by
- 25
- References
- 124
- Citations by year
- 2026: 18 · 2025: 6 · 2024: 1
- FWCI
- 6.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Molecular Systems Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (publishedVersion, cc-by)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Illinois Chicago (cc-by)
- Funders
- UKRI | Biotechnology and Biological Sciences Research Council (BB/X002179/1)
- National Science Foundation (NSF231427)
- HHS | NIH | National Human Genome Research Institute
- HHS | NIH | Division of Program Coordination, Planning, and Strategic Initiatives
- HHS | NIH | Office of Data Science Strategy
- HHS | NIH | National Institute of Allergy and Infectious Diseases
- HHS | NIH | National Institute on Aging
- HHS | NIH | National Institute of General Medical Sciences
- HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases
- HHS | NIH | National Eye Institute
- HHS | NIH | National Cancer Institute
- HHS | NIH | National Heart, Lung, and Blood Institute
- EMBL | European Bioinformatics Institute
- UKRI | Medical Research Council (MR/T03050X/1)
- Royal Society (RGS\R2\212303)
- HHS | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (R00 HD092613)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Keywords
- human protein complex, protein subunit composition, machine learning, AlphaFold, protein function prediction, protein expression
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