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Detecting Mutually Exclusive Interactions in Protein-Protein Interaction Maps

Overview of attention for article published in PLOS ONE, June 2012
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Title
Detecting Mutually Exclusive Interactions in Protein-Protein Interaction Maps
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0038765
Pubmed ID
Authors

Carmen Sánchez Claros, Anna Tramontano

Abstract

Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average) belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms.

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Geographical breakdown

Country Count As %
Germany 2 6%
United Kingdom 1 3%
Spain 1 3%
Brazil 1 3%
Unknown 29 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 35%
Student > Ph. D. Student 8 24%
Student > Bachelor 5 15%
Student > Postgraduate 3 9%
Student > Master 3 9%
Other 2 6%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 32%
Biochemistry, Genetics and Molecular Biology 8 24%
Computer Science 5 15%
Chemistry 3 9%
Medicine and Dentistry 3 9%
Other 2 6%
Unknown 2 6%