融合位置特征与序列进化信息的磷酸化位点预测
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湖南农业大学信息科学技术学院;湖南省农村农业信息化工程技术研究中心,湖南省长沙市芙蓉区湖南农业大学信息化建设与管理中心,湖南省长沙市芙蓉区湖南农业大学植物保护学院,湖南省长沙市芙蓉区湖南农业大学信息学院

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湖南省自然科学基金资助项目(14JJ2082)


Phosphorylation Site Prediction Integrating The Position Feature With Sequence Evolution Information
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College of Information Science and Technology,Hunan agricultural university;Hunan Engineering Research Center for Information Technology in Agriculture and rural,Center of Informatization Construction and management,Hunan agricultural university,College of Plant Protection,Hunan agricultural university,College of Information Science and Technology,Hunan agricultural university;Hunan Engineering Research Center for Information Technology in Agriculture and rural

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This work was supported by a grant from Natural Science Foundation of Hunan province (14JJ2082)

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    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χ2-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. χ2-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.

    Abstract:

    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, χ2-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 χ2-pos and the PsePSSM offers a promising method to predict phosphorylation sites more accurately in proteins.

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谭泗桥,李钎,陈渊,彭剑.融合位置特征与序列进化信息的磷酸化位点预测[J].生物化学与生物物理进展,2017,44(12):1118-1124

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历史
  • 收稿日期:2016-11-07
  • 最后修改日期:2017-08-20
  • 接受日期:2017-08-28
  • 在线发布日期: 2017-12-19
  • 出版日期: 2017-12-20