融合位置特征与序列进化信息的磷酸化位点预测

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．     

ProtIdent: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information

Proteases are vitally important to life cycles and have become a main target in drug development. According to their action mechanisms, proteases are classified into six types: (1) aspartic, (2) cysteine, (3) glutamic, (4) metallo, (5) serine, and (6) threonine. Given the sequence of an uncharacterized protein, can we identify whether it is a protease or non-protease? If it is, what type does it belong to? To address these problems, a 2-layer predictor, called "ProtIdent", is developed by fusing the functional domain and sequential evolution information: the first layer is for identifying the query protein as protease or non-protease; if it is a protease, the process will automatically go to the second layer to further identify it among the six types. The overall success rates in both cases by rigorous cross-validation tests were higher than 92%. ProtIdent is freely accessible to the public as a web server at http://www.csbio.sjtu.edu.cn/bioinf/Protease.