title: A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination.
abstract: Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets.
authors.affiliation: College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Li
authors.affiliation: College of Information Technology, Shanghai Ocean University, Shanghai 201306, China. Electronic address: tgliu@shou.edu.cn.
authors.forename: Taigang
authors.initials: T
authors.lastname: Liu
authors.affiliation: College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China.
authors.forename: Peiying
authors.initials: P
authors.lastname: Tao
authors.affiliation: College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Wang
authors.affiliation: College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China.
authors.forename: Lanming
authors.initials: L
authors.lastname: Chen
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D020539:Sequence Analysis, Protein
chemical_list: D011506:Proteins
keywords: Auto cross covariance; Low-similarity; Position-specific score matrix; Recursive feature elimination; Support vector machine