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Bioinformatics Advance Access published online on June 16, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti522
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received May 16, 2005
Accepted June 13, 2005

Article

Prediction of protein-protein interactions using distant conservation of sequence patterns and structure relationships

Jordi Espadaler 1, Oriol Romero-Isart 2, Richard M. Jackson 2*, and Baldo Oliva 1

1 Grup de Bioinformàtica Estructural (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain
2 School of Biochemistry and Microbiology, University of Leeds, Leeds LS2 9JT, United Kingdom

* To whom correspondence should be addressed.
Richard M. Jackson, E-mail: jackson{at}bmb.leeds.ac.uk


  Abstract

Motivation: Given that association and dissociation of protein molecules is crucial in most biological processes, recently, several in silico methods have been developed to predict protein-protein interactions. Structural evidence has shown that usually interacting pairs of close homologues (interologues) physically interact in the same way. Moreover, conservation of an interaction depends on the conservation of the interface between interacting partners. In this paper we make use of both structural similarities between 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 have split 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 (GO) 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.


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