Journal
COMPUTATIONAL BIOLOGY AND CHEMISTRY
Volume 59, Issue -, Pages 95-100Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compbiolchem.2015.08.012
Keywords
Low-similarity; Position-specific score matrix; Auto cross covariance; Support vector machine; Recursive feature elimination
Funding
- National Natural Science Foundation of China [41376135]
- Doctoral Fund of Ministry of Education of China [20133104110006]
- Innovation Program of Shanghai Municipal Education Commission [13YZ098]
- Foundation for University Youth Teachers of Shanghai [ZZhy12028]
- Doctoral Fund of Shanghai Ocean University
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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. (C) 2015 Elsevier Ltd. All rights reserved.
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