Predicting Protein-Protein Interactions from Protein Domains Using a Set Cover Approach
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Brief communication: RNA-binding residues in sequence space: Conservation and interaction patterns
Computational Biology and Chemistry
Using a stochastic adaboost algorithm to discover interactome motif pairs from sequences
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
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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@imim.es Supplementary information: http://sbi.imim.es/sup_mat/BioinformaticsO5_1/Supplementary_material.pdf