A Novel Kinase-substrate Relation Prediction Method Based on Substrate Sequence Similarity and Phosphorylation Network. Issue 28 (2015)

• Record Type: Journal Article
• Authors: Li, Haichun; Xu, Xiaoyi; Feng, Huanqing; Wang, Minghui
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 28(2015)
• Page Start: 17
• Page End: 21
• Publication Date: 2015
• Subjects: phosphorylation -- kinase-substrate relation -- heterogeneous networks -- HeteSim; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.12.093
• Languages: English
• ISSNs: 2405-8963

Abstract

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