title: Prediction of protein structural classes based on feature selection technique.
abstract: The prediction of protein structural classes is beneficial to understanding folding patterns, functions and interactions of proteins. In this study, we proposed a feature selection-based method to accurately predict protein structural classes. Three datasets with sequence identity lower than 25% were used to test the prediction performance of the method. Through jackknife cross-validation, we have verified that the overall accuracies of these three datasets are 92.1%, 89.7% and 84.0%, respectively. The proposed method is more efficient and accurate than other existing methods. The present study will offer an excellent alternative to other methods for predicting protein structural classes.
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Ding
authors.forename: Hao
authors.initials: H
authors.lastname: Lin
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.forename: Zi-Qiang
authors.initials: ZQ
authors.lastname: Li
authors.forename: Feng-Biao
authors.initials: FB
authors.lastname: Guo
authors.forename: Jian
authors.initials: J
authors.lastname: Huang
authors.forename: Nini
authors.initials: N
authors.lastname: Rao
mesh_terms: D000595:Amino Acid Sequence; D002965:Classification / Q000379:methods; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000145:classification*; D020539:Sequence Analysis, Protein; D060388:Support Vector Machine
chemical_list: D011506:Proteins