A map of human protein interactions derived from co‐expression of human mRNAs and their orthologs
Open accessAbstract
The human protein interaction network will offer global insights into the molecular organization of cells and provide a framework for modeling human disease, but the network's large scale demands new approaches. We report a set of 7000 physical associations among human proteins inferred from indirect evidence: the comparison of human mRNA co-expression patterns with those of orthologous genes in five other eukaryotes, which we demonstrate identifies proteins in the same physical complexes. To evaluate the accuracy of the predicted physical associations, we apply quantitative mass spectrometry shotgun proteomics to measure elution profiles of 3013 human proteins during native biochemical fractionation, demonstrating systematically that putative interaction partners tend to co-sediment. We further validate uncharacterized proteins implicated by the associations in ribosome biogenesis, including WBSCR20C, associated with Williams-Beuren syndrome. This meta-analysis therefore exploits non-protein-based data, but successfully predicts associations, including 5589 novel human physical protein associations, with measured accuracies of 54+/-10%, comparable to direct large-scale interaction assays. The new associations' derivation from conserved in vivo phenomena argues strongly for their biological relevance.
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Signed networks are more predictive of protein interactions and shared pathway relationships than unsigned networks.
“signed networks are more predictive of protein interactions and shared pathway relationships than unsigned networks38,42,43”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [43]
There was no pathway or PPI enrichment in these modules, probably owing to inclusion of both positive and negative correlations when computing co-expression relationships (unsigned networks), which is less sensitive to pathway and protein interaction detection.
“Interestingly, there was no pathway or PPI enrichment in these modules, probably owing to the inclusion of both positive and negative correlations when computing co-expression relationships (unsigned networks), which is a method that is less sensitive to pathway and protein interaction detection38,43.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [43]
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- Authors
- Ramani, A. K., Li, Z., Hart, G. T., Carlson, M. W., Boutz, D. R., & Marcotte, E. M.
- Affiliations
- The University of Texas at Austin (US)
- Corresponding author
- Marcotte, Edward M
- Venue
- Molecular Systems Biology
- Published
- 2008-04-15
- Volume
- 4
- Issue
- 1
- Pages
- 180-180
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC2387231
- OpenAlex
- W2040581784
- Cited by
- 78
- References
- 76
- Citations by year
- 2026: 1 · 2022: 2 · 2021: 5 · 2019: 6 · 2018: 4 · 2017: 2 · 2016: 6 · 2015: 4 · 2014: 2 · 2013: 4 · 2012: 12
- FWCI
- 4.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Systems Biology (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation
- National Institute of General Medical Sciences (R01GM076536)
- 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
- protein-protein interaction network, orthologs, protein complexes, shotgun proteomics, ribosome biogenesis, Williams syndrome, cross-species comparison
- MeSH terms
- Animals, Chromosome Mapping, HeLa Cells, Humans, Models, Biological, Proteins, RNA, Messenger, Mass Spectrometry, Reproducibility of Results, Models, Statistical, Gene Expression, Cluster Analysis, Gene Expression Profiling, Protein Interaction Mapping, Proteomics
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