Secondary Structure Element Alignment Kernel Method for Prediction of Protein Structural Classes
Secondary Structure Element Alignment Kernel Method for Prediction of Protein Structural Classes
复制标题
用于预测蛋白质结构类别的二级结构元素比对核方法
DOI：
10.2174/1574893609999140523124847
复制
发表时间：
2014-06
期刊：
影响因子：
--
通讯作者：
Vo Anh
中科院分区：
文献类型：
--
作者：
Guo-Sheng Han;Zu-Guo Yu;Vo Anh
在本文中，我们的目标是根据预测的二级结构预测低同源性数据集的蛋白质结构类别。我们提出了一种新的简单核方法，称为 SSEAKSVM，来预测蛋白质结构类别。利用PSIPRED工具获得所有蛋白质序列的二级结构，然后构建基于二级结构元素比对分数的线性核，用于训练支持向量机分类器而无需调整参数。我们的方法 SSEAKSVM 在两个低同源性数据集 25PDB 和 1189 上进行了评估，序列同源性分别为 25% 和 40%。折刀测试用于测试我们的方法并将其与其他现有方法进行比较。这两个数据集的总体准确率分别为 86.3% 和 84.5%，高于其他现有方法获得的结果。特别是，与其他方法相比，我们的方法在区分 α + β 类和 α/β 类方面实现了更高的准确度（88.1% 和 88.5%）。这表明我们的方法对于预测蛋白质结构类别很有价值，特别是对于低同源性蛋白质序列。本文方法的源代码可以在http://math.xtu.edu.cn/myphp/math/research/source/SSEAK_source_code.rar下载。
In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α + β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://math.xtu.edu.cn/myphp/math/research/source/SSEAK_source_code.rar.