Protein Phosphorylation Site Prediction via Feature Discovery Support Vector Machine

作     者：Yi Shi Bo Yuan Guohui Lin Dale Schuurmans 

作者机构：Department of Computing Science University of Alberta Edmonton T6G 2E8 Canada Department of Computer Science Shanghai Jiao Tong University Shanghai 200240 China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2012年第17卷第6期

页      面：638-644页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 081704[工学-应用化学] 07[理学] 08[工学] 0817[工学-化学工程与技术] 09[农学] 0902[农学-园艺学] 090202[农学-蔬菜学] 

主　　题：protein phosphorylation support vector machine sparse learning feature selection position-specificscoring matrix 

摘      要：Protein phosphorylation/dephosphorylation is the central mechanism of post-translational modification which regulates cellular responses and phenotypes. Due to the efficiency and resource constraints of the in vivo methods for identifying phosphorylation sites, there is a strong motivation to computationally predict potential phosphorylation sites. In this work, we propose to use a unique set of features to represent the peptides surrounding the amino acid sites of interest and use feature selection support vector machine to predict whether the serine/threonine sites are potentially phosphorylable, as well as selecting important features that may lead to phosphorylation. Experimental results indicate that the new features and the prediction method can more effectively predict protein phosphorylation sites than the existing state of the art methods. The features selected by our prediction model provide biological insights to the in vivo phosphorylation.