Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network

被引:155
作者
Chen, Chao [1 ]
Zhou, Xibin [1 ]
Tian, Yuanxin [1 ]
Zou, Xiaoyong [1 ]
Cai, Peixiang [1 ]
机构
[1] Sun Yat Sen Univ, Sch Chem & Chem Engn, Guangzhou 510275, Peoples R China
基金
中国国家自然科学基金;
关键词
support vector machine; fusion; amino acid composition; pair-coupled amino acid composition; pseudo-amino acid composition; protein structural class;
D O I
10.1016/j.ab.2006.07.022
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Because a priori knowledge of a protein structural class can provide useful information about its overall structure, the determination of protein structural class is a quite meaningful topic in protein science. However, with the rapid increase in newly found protein sequences entering into databanks, it is both time-consuming and expensive to do so based solely on experimental techniques. Therefore, it is vitally important to develop a computational method for predicting the protein structural class quickly and accurately. To deal with the challenge, this article presents a dual-layer support vector machine (SVM) fusion network that is featured by using a different pseudo-amino acid composition (PseAA). The PseAA here contains much information that is related to the sequence order of a protein and the distribution of the hydrophobic amino acids along its chain. As a showcase, the rigorous jackknife cross-validation test was performed on the two benchmark data sets constructed by Zhou. A significant enhancement in success rates was observed, indicating that the current approach may serve as a powerful complementary tool to other existing methods in this area. (c) 2006 Elsevier Inc. All rights reserved.
引用
收藏
页码:116 / 121
页数:6
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