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Genome-Wide Prediction of SH2 Domain Targets Using Structural Information and the FoldX Algorithm

Overview of attention for article published in PLoS Computational Biology, April 2008
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Title
Genome-Wide Prediction of SH2 Domain Targets Using Structural Information and the FoldX Algorithm
Published in
PLoS Computational Biology, April 2008
DOI 10.1371/journal.pcbi.1000052
Pubmed ID
Authors

Ignacio E. Sánchez, Pedro Beltrao, Francois Stricher, Joost Schymkowitz, Jesper Ferkinghoff-Borg, Frederic Rousseau, Luis Serrano

Abstract

Current experiments likely cover only a fraction of all protein-protein interactions. Here, we developed a method to predict SH2-mediated protein-protein interactions using the structure of SH2-phosphopeptide complexes and the FoldX algorithm. We show that our approach performs similarly to experimentally derived consensus sequences and substitution matrices at predicting known in vitro and in vivo targets of SH2 domains. We use our method to provide a set of high-confidence interactions for human SH2 domains with known structure filtered on secondary structure and phosphorylation state. We validated the predictions using literature-derived SH2 interactions and a probabilistic score obtained from a naive Bayes integration of information on coexpression, conservation of the interaction in other species, shared interaction partners, and functions. We show how our predictions lead to a new hypothesis for the role of SH2 domains in signaling.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 4%
United States 2 2%
Chile 1 1%
France 1 1%
Portugal 1 1%
Israel 1 1%
Canada 1 1%
Singapore 1 1%
Switzerland 1 1%
Other 4 5%
Unknown 66 80%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 27%
Researcher 18 22%
Professor 9 11%
Professor > Associate Professor 9 11%
Student > Master 7 9%
Other 10 12%
Unknown 7 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 49%
Biochemistry, Genetics and Molecular Biology 14 17%
Computer Science 7 9%
Chemistry 5 6%
Immunology and Microbiology 2 2%
Other 5 6%
Unknown 9 11%