Bloom, Jesse D. and Adami, Christoph (2003) Apparent dependence of protein evolutionary rate on number of interactions is linked to biases in protein–protein interactions data sets. BMC Evolutionary Biology, 3 (21). ISSN 1471-2148 http://resolver.caltech.edu/CaltechAUTHORS:BLObmceb03
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Background: Several studies have suggested that proteins that interact with more partners evolve more slowly. The strength and validity of this association has been called into question. Here we investigate how biases in high-throughput protein–protein interaction studies could lead to a spurious correlation. Results: We examined the correlation between evolutionary rate and the number of protein–protein interactions for sets of interactions determined by seven different high-throughput methods in Saccharomyces cerevisiae. Some methods have been shown to be biased towards counting more interactions for abundant proteins, a fact that could be important since abundant proteins are known to evolve more slowly. We show that the apparent tendency for interactive proteins to evolve more slowly varies directly with the bias towards counting more interactions for abundant proteins. Interactions studies with no bias show no correlation between evolutionary rate and the number of interactions, and the one study biased towards counting fewer interactions for abundant proteins actually suggests that interactive proteins evolve more rapidly. In all cases, controlling for protein abundance significantly decreases the observed correlation between interactions and evolutionary rate. Finally, we disprove the hypothesis that small data set size accounts for the failure of some interactions studies to show a correlation between evolutionary rate and the number of interactions. Conclusions: The only correlation supported by a careful analysis of the data is between evolutionary rate and protein abundance. The reported correlation between evolutionary rate and protein–protein interactions cannot be separated from the biases of some protein–protein interactions studies to count more interactions for abundant proteins.
|Additional Information:||© 2003 Bloom and Adami; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. Authors' Contributions: JDB gathered the data, performed the statistical analysis, and wrote the manuscript. CA provided guidance on the analysis and edited the manuscript. Both authors read and approved the final manuscript. We thank Frances H. Arnold for helpful comments and advice. We also thank an anonymous reviewer for insightful comments that greatly improved our work. JDB is supported by a Howard Hughes Medical Institute Predoctoral Fellowship. CA is supported by the NSF under contract number DEB-9981397. Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.|
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|Deposited On:||22 Nov 2005|
|Last Modified:||26 Dec 2012 08:42|
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