Signaling Pathway Prediction by Path Frequency in Protein-Protein Interaction Networks

A signaling pathway, which is represented as a chain of interacting proteins for a biological process, can be predicted from protein-protein interaction (PPI) networks. However, pathway prediction is computationally challenging because of (1) inefficiency in searching all possible paths from the large-scale PPI networks and (2) unreliability of current PPI data generated by automated high-throughput methods. In this paper, we propose a novel approach to efficiently predict signaling pathways from PPI networks when a starting protein (source) and an ending protein (target) are given. Our approach is a combination of topological analysis of the networks and ontological analysis of interacting proteins. Starting from the source, this method repeatedly extends the list of proteins to form a pathway based on the improved support model (iSup). This model integrates (1) the frequency of the paths towards the target and (2) the semantic similarity between each adjacent pair in a pathway. The path frequency is computed by a heuristic data-mining technique to determine the most frequent paths towards the target in a PPI network. The semantic similarity is measured by the distance of the information contents of Gene Ontology (GO) terms annotating interacting proteins. To further improve computational efficiency, we propose two additional strategies: filtering the PPI networks and pre-computing approximate path frequency. The experiment with the yeast PPI data demonstrates that our approach predicted MAPK signaling pathways with higher accuracy and efficiency than other existing methods.