Bioinformatics Advance Access originally published online on June 16, 2005
Bioinformatics 2005 21(16):3360-3368; doi:10.1093/bioinformatics/bti522
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Prediction of protein–protein interactions using distant conservation of sequence patterns and structure relationships
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1Grup de Bioinformàtica Estructural (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra Barcelona 08003, Catalonia, Spain
2School of Biochemistry and Microbiology, University of Leeds Leeds LS2 9JT, UK
*To whom correspondence should be addressed.
Motivation: Given that association and dissociation of protein molecules is crucial in most biological processes several in silico methods have been recently developed to predict protein–protein interactions. Structural evidence has shown that usually interacting pairs of close homologs (interologs) physically interact in the same way. Moreover, conservation of an interaction depends on the conservation of the interface between interacting partners. In this article we make use of both, structural similarities among domains of known interacting proteins found in the Database of Interacting Proteins (DIP) and conservation of pairs of sequence patches involved in protein–protein interfaces to predict putative protein interaction pairs.
Results: We have obtained a large amount of putative protein–protein interaction (
130 000). The list is independent from other techniques both experimental and theoretical. We separated the list of predictions into three sets according to their relationship with known interacting proteins found in DIP. For each set, only a small fraction of the predicted protein pairs could be independently validated by cross checking with the Human Protein Reference Database (HPRD). The fraction of validated protein pairs was always larger than that expected by using random protein pairs. Furthermore, a correlation map of interacting protein pairs was calculated with respect to molecular function, as defined in the Gene Ontology database. It shows good consistency of the predicted interactions with data in the HPRD database. The intersection between the lists of interactions of other methods and ours produces a network of potentially high-confidence interactions.
Contact: boliva{at}imim.es
Supplementary information: http://sbi.imim.es/sup_mat/BioinformaticsO5_1/Supplementary_material.pdf
Received on May 16, 2005; accepted on June 13, 2005
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