A Novel Kinase-substrate Relation Prediction Method Based on Substrate Sequence Similarity and Phosphorylation Network. Issue 28 (2015) - Li, Haichun; Xu, Xiaoyi; Feng, Huanqing; Wang, Minghui
Abstract: Protein phosphorylation catalyzed by kinases plays essential roles in various intracellular processes. Therefore, the identification of potential relations between kinases and substrates is one of the key areas in post-translational modifications. Although a number of computational approaches have been designed, most existing kinase-substrate relation (KSR) prediction methods only focus on protein sequence information without considering kinase-substrate network. In this paper, we proposed a novel KSR prediction method called HeteSim-S based both substrate sequence similarity and phosphorylation heterogeneous network through HeteSim algorithm, which has been used in previous studies of similar search. Experiment results in kinase-substrate heterogeneous network show that our method can effectively predict kinase-substrate relations with the AUC measure achieving 0.842. Besides, the AUC performance on specific kinases is up to 0.971. The result demonstrates that HeteSim-S can remarkably improve the identification accuracy by incorporating substrate sequence similarity information in kinasesubstrate heterogeneous networks