Inferring protein–protein interactions through high-throughput interaction data from diverse organisms

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Abstract
Motivation: Identifying protein–protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain–domain and protein–protein interaction probabilities. Results: We use a likelihood approach to estimating domain–domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain–domain interaction probabilities are then used to predict protein–protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein–protein interactions from diverse organisms than from a single organism. Availability: The program for computing the protein–protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction Contact:hongyu.zhao@yale.edu