title: A novel protein structural classes prediction method based on predicted secondary structure.
abstract: 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 α/β class and α + β 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 α/β and α + β 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.
authors.affiliation: School of Mathematical Sciences, Dalian University of Technology, Linggong Road, Dalian, 116024, PR China. sunnyday1979@163.com
authors.forename: Shuyan
authors.initials: S
authors.lastname: Ding
authors.forename: Shengli
authors.initials: S
authors.lastname: Zhang
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.forename: Tianming
authors.initials: T
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D011506:Proteins