融合位置特征与序列进化信息的磷酸化位点预测（英文）  

Phosphorylation Site Prediction Integrating The Position Feature With Sequence Evolution Information

作　　者：谭泗桥[1,2] 李钎 陈渊 彭剑[1,2] TAN Si-Qiao;LI Qian;CHEN Yuan;PENG Jian(7 College of Information Science and Technology, Hunan Agricultural University, Changsha 410128, China;Hunan Engineering Research Center for Information Technology in Agriculture and Rural, Changsha 410128, China;Center of Information Construction and Management, Hunan Agricultural University, Changsha 410128, China;College of Plant Protection, Hunan Agricultural University, Changsha 410128, China)

机构地区：[1]湖南农业大学信息科学技术学院,长沙410128 [2]湖南省农村农业信息化工程技术研究中心,长沙410128 [3]湖南农业大学信息化建设与管理中心,长沙410128 [4]湖南农业大学植物保护学院,长沙410128

出　　处：《生物化学与生物物理进展》2017年第12期1118-1124,共7页Progress In Biochemistry and Biophysics

基　　金：supported by a grant from Natural Science Foundation of Hunan province(14JJ2082)~~

摘　　要：磷酸化是蛋白质翻译后的主要修饰,可分为激酶特异性和非激酶特异性两种类型.以非激酶特异性磷酸化位点Dou数据集为基础,本文发展了一种基于位置的卡方差表特征χ~2-pos,融合伪氨基酸序列进化信息PsePSSM表征序列,构建正负样本均衡的支持向量机分类器,S,T,Y独立测试Matthew相关系数、ROC曲线下面积分及准确率分别达到了(0.59、0.87、79.74%),(0.55、0.85、77.68%)和(0.50、0.81、75.22%),明显优于文献报道结果.χ~2-pos、PsePSSM两种特征的融合在蛋白质磷酸化位点预测中有广泛应用前景.Phosphorylation is the major post-translation modification to proteins, and it can be classified as kinase-specific and non-kinase-specific. This paper focuses on the prediction methods of non-kinase-specificity and using Dou＇s dataset of phosphorylation sites as the template, this paper develops a position-based chi-square table feature, χ~2-pos, and then integrates this feature with the pseudo position-specific scoring matrix（PsePSSM）. A Support Vector Machine（SVM） classifier with balanced positive and negative samples was created, and the S, T, Y independent testing results for the Matthew correlation coefficient, the inferior surface integral of the ROC curve and the precision were（0.59, 0.87, 79.74%）,（0.55, 0.85, 77.68%） and（0.50, 0.81, 75.22%）, respectively, which are significantly superior to the results reported previously. The integration of the χ~2-pos and the PsePSSM offers a promising method to predict phosphorylation sites more accurately in proteins.

关 键 词：磷酸化 预测 卡方差表特征 伪氨基酸序列进化信息 支持向量机 

分 类 号：Q51[生物学—生物化学] Q61