Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae
Open accessAbstract
Defining protein complexes is critical to virtually all aspects of cell biology.Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data.The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives.Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction.This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments.For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence.Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies.Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering.This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community. Molecular & Cellular Proteomics 6:439 -450, 2007.Because most cellular functions are mediated by groups of physically associated proteins or complexes that work in a coherent fashion, it is of great interest to systematically map protein-protein interactions (PPIs). 1 In Saccharomyces cerevi-
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Y2H, APMS, and other high-throughput experimental approaches have identified many pairs of interacting proteins in yeast and other organisms, but there are discrepancies between sets generated using different methods and considerable false positive and false negative rates.
“Yeast two hybrid (Y2H), affinity-purification mass spectrometry (APMS), and other high-throughput experimental approaches have identified many pairs of interacting proteins in yeast and other organisms (1–5), but there are discrepancies between sets generated using the different methods and considerable false positive and false negative rates (6–8).”
From Computed structures of core eukaryotic protein complexes (2021) · cites this paper as [2]
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- Authors
- Collins, S. R., Kemmeren, P., Zhao, X.-C., Greenblatt, J. F., Spencer, F., Holstege, F. C. P., Weissman, J. S., & Krogan, N. J.
- Affiliations
- Howard Hughes Medical Institute (US)
- University of California, San Francisco (US)
- University of Toronto (CA)
- Johns Hopkins University (US)
- QB3 (US)
- Corresponding author
- Weissman, Jonathan S.
- Krogan, Nevan J.
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2007-03
- Volume
- 6
- Issue
- 3
- Pages
- 439-450
- ISSN
- 1535-9476 (ISSN-L)
- OpenAlex
- W2116447934
- Cited by
- 795
- References
- 57
- Citations by year
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- FWCI
- 31.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Molecular & Cellular Proteomics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- 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 › Molecular Biology › Fungal and yeast genetics research
- Keywords
- protein-protein interaction, protein complexes, Saccharomyces cerevisiae, BioGRID, affinity purification, false positives, hierarchical clustering
- MeSH terms
- Saccharomyces cerevisiae, Cluster Analysis, Proteome, Protein Interaction Mapping, Saccharomyces cerevisiae Proteins, Databases, Protein
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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