A novel protein structural classes prediction method based on predicted secondary structure

作者：Ding, Shuyan^*; Zhang, Shengli; Li, Yang; Wang, Tianming

来源：Biochimie, 2012, 94(5): 1166-1171.

DOI：10.1016/j.biochi.2012.01.022

• Protein structural classes
• Support vector machine
• Feature selection

Knowledge of structural classes plays an important role in understanding protein folding patterns. In this paper, features based on the predicted secondary structure sequence and the corresponding E-H sequence are extracted. Then, an 11-dimensional feature vector is selected based on a wrapper feature selection algorithm and a support vector machine (SVM). Among the 11 selected features, 4 novel features are newly designed to model the differences between alpha/beta class and alpha + beta class, and other 7 rational features are proposed by previous researchers. To examine the performance of our method, a total of 5 datasets are used to design and test the proposed method. The results show that competitive prediction accuracies can be achieved by the proposed method compared to existing methods (SCPRED, RKS-PPSC and MODAS), and 4 new features are demonstrated essential to differentiate alpha/beta and alpha + beta classes. Standalone version of the proposed method is written in JAVA language and it can be downloaded from http://web.xidian.edu.cn/slzhang/paper.html.

• 出版日期2012-5
• 单位西安电子科技大学; 大连民族大学; 大连理工大学