title: Novel structure-driven features for accurate prediction of protein structural class.
abstract: Prediction of protein structural class plays an important role in inferring tertiary structure and function of a protein. Extracting good representation from protein sequence is fundamental for this prediction task. In this paper, a novel computational method is proposed to predict protein structural class solely from the predicted secondary structure information. A total of 27 features rationally divided into 3 different groups are extracted to characterize general contents and spatial arrangements of the predicted secondary structural elements. Then, a multi-class nonlinear support vector machine classifier is used to implement prediction. Various prediction accuracies evaluated by the jackknife cross-validation test are reported on four widely-used low-homology benchmark datasets. Comparing with the state-of-the-art in protein structural class prediction, the proposed method achieves the highest overall accuracies on all the four datasets. The experimental results confirm that the proposed structure-driven features are very useful for accurate prediction of protein structural class.
authors.affiliation: College of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, PR China.
authors.forename: Liang
authors.initials: L
authors.lastname: Kong
authors.affiliation: College of Marine Life Science, Ocean University of China, Yushan Road, Qingdao 266003, PR China. Electronic address: zhanglichaoouc@126.com.
authors.forename: Lichao
authors.initials: L
authors.lastname: Zhang
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008969:Molecular Sequence Data; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D011506:Proteins
keywords: Protein domains; Protein sequence homology; Secondary protein structure; Support vector machines