A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC
A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC
复制标题
通过将氨基酸分类和理化特性纳入 Chou 的 PseAAC 的一般形式来预测膜蛋白类型的两阶段 SVM 方法
DOI：
10.1016/j.jtbi.2013.11.017
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发表时间：
2014-03-07
影响因子：
2
通讯作者：
Vo Anh
中科院分区：
文献类型：
--
作者：
Han, Guo-Sheng;Yu, Zu-Guo;Vo Anh
膜蛋白在许多生化过程中发挥着重要作用，也是治疗各种疾病的药物开发的诱人靶点。膜蛋白类型的阐明为理解蛋白质的结构和功能提供了线索。最近，我们开发了一种新的预测蛋白质亚核定位的系统。在这篇文章中，我们提出了一个简化版本的我们的系统，用于直接从一级蛋白质结构预测膜蛋白类型，它将氨基酸分类和物理化学性质合并到假性氨基酸组成的一般形式中。在这个简化的系统中，我们将设计一个结合两步最优特征选择过程的两阶段多类支持向量机，这在我们的实验中被证明是非常有效的。在由五种膜蛋白组成的两个基准数据集上对本方法的性能进行了评估。通过刀切检验和独立数据集检验，五种类型的预测总准确率分别为93.25%和96.61%。这些结果表明，我们的方法在预测膜蛋白类型方面是有效的和有价值的。Http://www.juemengt.com/jcc/memty_page.php(C)2013爱思唯尔有限公司提供了用于建议方法的网络服务器。保留所有权利。
Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php (C) 2013 Elsevier Ltd. All rights reserved.