title: A protein structural class prediction method based on novel features.
abstract: In this study, a 12-dimensional feature vector is constructed to reflect the general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Among the 12 features, 6 novel features are specially designed to improve the prediction accuracies for α/β and α + β classes based on the distributions of α-helices and β-strands and the characteristics of parallel β-sheets and anti-parallel β-sheets. To evaluate our method, the jackknife cross-validating test is employed on two widely-used datasets, 25PDB and 1189 datasets with sequence similarity lower than 40% and 25%, respectively. The performance of our method outperforms the recently reported methods in most cases, and the 6 newly-designed features have significant positive effect to the prediction accuracies, especially for α/β and α + β classes.
authors.affiliation: College of Marine Life Science, Ocean University of China, Yushan Road, Qingdao 266003, PR China.
authors.forename: Lichao
authors.initials: L
authors.lastname: Zhang
authors.forename: Xiqiang
authors.initials: X
authors.lastname: Zhao
authors.forename: Liang
authors.initials: L
authors.lastname: Kong
mesh_terms: D019295:Computational Biology / Q000379:methods*; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D011506:Proteins
keywords: Protein structural class; Secondary structure; Sequence similarity; Support vector machine