Predicting protein–protein interactions using signature products

Proteome-wide prediction of protein-protein interaction is a difficult and important problem in biology. Although there have been recent advances in both experimental and computational methods for predicting protein-protein interactions, we are only beginning to see a confluence of these techniques. In this paper, we describe a very general, high-throughput method for predicting protein-protein interactions. Our method combines a sequence-based description of proteins with experimental information that can be gathered from any type of protein-protein interaction screen. The method uses a novel description of interacting proteins by extending the signature descriptor, which has demonstrated success in predicting peptide/protein binding interactions for individual proteins. This descriptor is extended to protein pairs by taking signature products. The signature product is implemented within a support vector machine classifier as a kernel function.