title: High-accuracy prediction of protein structural class for low-similarity sequences based on predicted secondary structure.
abstract: Information on the structural classes of proteins has been proven to be important in many fields of bioinformatics. Prediction of protein structural class for low-similarity sequences is a challenge problem. In this study, 11 features (including 8 re-used features and 3 newly-designed features) are rationally utilized to reflect the general contents and spatial arrangements of the secondary structural elements of a given protein sequence. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark datasets, 1189 and 25PDB with sequence similarity lower than 40% and 25%, respectively. Comparison of our results with other methods shows that our proposed method is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity datasets.
authors.affiliation: School of Mathematical Sciences, Dalian University of Technology, Ganjingzi District, Dalian, Liaoning, PR China. shengli0201@163.com
authors.forename: Shengli
authors.initials: S
authors.lastname: Zhang
authors.forename: Shuyan
authors.initials: S
authors.lastname: Ding
authors.forename: Tianming
authors.initials: T
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology; D030562:Databases, Protein; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment
chemical_list: D011506:Proteins