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What Evidence Is There for the Homology of Protein-Protein Interactions?

Overview of attention for article published in PLoS Computational Biology, September 2012
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Title
What Evidence Is There for the Homology of Protein-Protein Interactions?
Published in
PLoS Computational Biology, September 2012
DOI 10.1371/journal.pcbi.1002645
Pubmed ID
Authors

Anna C. F. Lewis, Nick S. Jones, Mason A. Porter, Charlotte M. Deane

Abstract

The notion that sequence homology implies functional similarity underlies much of computational biology. In the case of protein-protein interactions, an interaction can be inferred between two proteins on the basis that sequence-similar proteins have been observed to interact. The use of transferred interactions is common, but the legitimacy of such inferred interactions is not clear. Here we investigate transferred interactions and whether data incompleteness explains the lack of evidence found for them. Using definitions of homology associated with functional annotation transfer, we estimate that conservation rates of interactions are low even after taking interactome incompleteness into account. For example, at a blastp E-value threshold of 10(-70), we estimate the conservation rate to be about 11 % between S. cerevisiae and H. sapiens. Our method also produces estimates of interactome sizes (which are similar to those previously proposed). Using our estimates of interaction conservation we estimate the rate at which protein-protein interactions are lost across species. To our knowledge, this is the first such study based on large-scale data. Previous work has suggested that interactions transferred within species are more reliable than interactions transferred across species. By controlling for factors that are specific to within-species interaction prediction, we propose that the transfer of interactions within species might be less reliable than transfers between species. Protein-protein interactions appear to be very rarely conserved unless very high sequence similarity is observed. Consequently, inferred interactions should be used with care.

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Geographical breakdown

Country Count As %
United States 6 4%
Germany 3 2%
United Kingdom 3 2%
Spain 2 1%
Colombia 2 1%
Canada 2 1%
India 1 <1%
Netherlands 1 <1%
France 1 <1%
Other 4 2%
Unknown 143 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 50 30%
Student > Ph. D. Student 47 28%
Student > Master 17 10%
Student > Bachelor 14 8%
Professor 7 4%
Other 19 11%
Unknown 14 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 82 49%
Biochemistry, Genetics and Molecular Biology 38 23%
Computer Science 10 6%
Chemistry 7 4%
Mathematics 3 2%
Other 9 5%
Unknown 19 11%