Prediction of signaling networks by information propagation on protein-protein interaction networks integrated with GO annotations

The experimental study of signal transduction over a decade has made a substantial contribution to understanding functional mechanisms in a cell. A signaling pathway represents a linear path of a signaling cascade involving a series of proteins. As an advanced model, multiple linear pathways with extensive cross-talk between receptors can be merged into a larger-scale signaling network. We present an efficient computational approach to predict signaling networks by integration of genome-wide protein-protein interaction (PPI) data and ontological annotation data. We adopt an advanced semantic similarity metric for weighting PPIs, and an information propagation algorithm that runs on a weighted PPI network. This algorithm iteratively selects potential directed edges for signaling cascade using user-specified path strength parameters. Our approach also includes a preprocessing step to filter the large-scale PPI network by distance condition using the maximum path length parameter. Our experimental results show that the proposed approach runs extremely faster than existing computational methods and has competitive accuracy in the test of predicting well-studied pathways of S. cerevisiae and C. elegans. High efficiency of this approach would facilitate development of a web-based application tool to discover potential signaling networks.