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

Chao Chen·Xibin Zhou·Yuanxin Tian·Xiaoyong Zou·Peixiang Cai
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Abstract

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.

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AlgorithmsAmino AcidsComputing MethodologiesProteinsJournal ArticleResearch Support, Non-U.S. Gov't

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Chao Chen, Xibin Zhou, Yuanxin Tian, Xiaoyong Zou, Peixiang Cai. 2006-08-07. Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network.. https://doi.org/10.1016/j.ab.2006.07.022

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