A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination. (December 2015)

• Record Type: Journal Article
• Authors: Li, Xiaowei; Liu, Taigang; Tao, Peiying; Wang, Chunhua; Chen, Lanming
• Is Part Of: Computational biology and chemistry. Volume 59:Part A(2015)
• Page Start: 95
• Page End: 100
• Publication Date: 2015-12
• Subjects: Low-similarity -- Position-specific score matrix -- Auto cross covariance -- Support vector machine -- Recursive feature elimination; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.08.012
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Prediction performance of protein structural class has been improved. A high-quality feature extraction technique has been designed. A recursive feature selection has been used to reduce feature abundance. 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.