A Novel Kinase-substrate Relation Prediction Method Based on Substrate Sequence Similarity and Phosphorylation Network

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• 作者：Haichun Li^* ; Xiaoyi Xu^* ; Huanqing Feng^* ; Minghui Wang^** ; ^{mhwang@ustc.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：phosphorylation ; kinase-substrate relation ; heterogeneous networks ; HeteSim
• 刊名：IFAC-PapersOnLine
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：48
• 期：28
• 页码：17-21
• 全文大小：775 K

文摘

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