Qiu Jian-Ding, Luo San-Hua, Huang Jian-Hua, Liang Ru-Ping
Institute for Advanced Study and Department of Chemistry, Nanchang University, Nanchang 330031, People's Republic of China.
J Comput Chem. 2009 Jun;30(8):1344-50. doi: 10.1002/jcc.21115.
The prediction of secondary structure is a fundamental and important component in the analytical study of protein structure and functions. How to improve the predictive accuracy of protein structural classification by effectively incorporating the sequence-order effects is an important and challenging problem. In this study, a new method, in which the support vector machine combines with discrete wavelet transform, is developed to predict the protein structural classes. Its performance is assessed by cross-validation tests. The predicted results show that the proposed approach can remarkably improve the success rates, and might become a useful tool for predicting the other attributes of proteins as well.
二级结构预测是蛋白质结构与功能分析研究中的一个基本且重要的组成部分。如何通过有效纳入序列顺序效应来提高蛋白质结构分类的预测准确性是一个重要且具有挑战性的问题。在本研究中，开发了一种支持向量机与离散小波变换相结合的新方法来预测蛋白质结构类别。通过交叉验证测试评估其性能。预测结果表明，所提出的方法能够显著提高成功率，并且可能成为预测蛋白质其他属性的有用工具。
