title: A high-accuracy protein structural class prediction algorithm using predicted secondary structural information.
abstract: One major problem with the existing algorithm for the prediction of protein structural classes is low accuracies for proteins from α/β and α+β classes. In this study, three novel features were rationally designed to model the differences between proteins from these two classes. In combination with other rational designed features, an 11-dimensional vector prediction method was proposed. By means of this method, the overall prediction accuracy based on 25PDB dataset was 1.5% higher than the previous best-performing method, MODAS. Furthermore, the prediction accuracy for proteins from α+β class based on 25PDB dataset was 5% higher than the previous best-performing method, SCPRED. The prediction accuracies obtained with the D675 and FC699 datasets were also improved.
authors.affiliation: Department of Bioscience and Biotechnology, Dalian University of Technology, No 2 Linggong road, Dalian 116024, China.
authors.forename: Tian
authors.initials: T
authors.lastname: Liu
authors.forename: Cangzhi
authors.initials: C
authors.lastname: Jia
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000145:classification*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins