Enhancing the prediction of protein pairings between interacting families using orthology information

For the first time in methods for predicting mappings, we have tested this new approach on a large number of interacting protein domains in order to statistically assess its performance. The method accurately predicts around 80% in the most favourable cases. We also analysed in detail the results of the method for a well defined case of interacting families, the sensor and kinase components of the Ntr-type two-component system, for which up to 98% of the pairings predicted by the method were correct.Based on the well established relationship between tree similarity and interactions we developed a method for predicting the mapping between two interacting families using genomic information alone. The program is available through a web interface.The biological function of many proteins can only be understood in the context of their relationships with others. For this reason the biological knowledge represented in the "interactome" (the set of protein interactions for a given proteome) cannot be derived from the properties of the isolated proteins. In recent years, efforts have attempted to decipher networks of protein interactions using experimental and computational approaches [1].One of computational techniques for predicting and studying protein interactions widely accepted by the community is that based on the similarity of phylogenetic trees. This arose from the initial qualitative observation of topological similarities between the phylogenetic trees of a number of interacting protein families [2-4]. Later these observations were systematized and quantified, and the use of tree-similarity based methods to separate interacting and non-interacting families in large sets of proteins was proposed [5,6]. This original mirrortree approach was subsequently improved in different ways, for example by introducing a correction for the background similarity created by the underlying speciation events [7,8]. The hypothesis that explains the relationship observed between tree