A motif-based profile scanning approach for genome-wide prediction of signaling pathways

The rapid increase in genomic information requires new techniques to infer protein function and predict protein-protein interactions. Bioinformatics i dentifies modular signaling domains within protein sequences with a high de gree of accuracy. In contrast, little success has been achieved in predicti ng short linear sequence motifs within proteins targeted by these domains t o form complex signaling networks. Here we describe a peptide library-based searching algorithm, accessible over the World Wide Web, that identifies s equence motifs likely to bind to specific protein domains such as 14-3-3, S H2, and SH3 domains, or likely to be phosphorylated by specific protein kin ases such as Src and AKT Predictions from database searches for proteins co ntaining motifs matching two different domains in a common signaling pathwa y provides a much higher success rate. This technology facilitates predicti on of cell signaling networks within proteomes, and could aid in the identi fication of drug targets for the treatment of human diseases.