Protein remote homology detection by combining Chou’s distance-pair pseudo amino acid composition and principal component analysis

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• 作者：Bin Liu ; Junjie Chen ; Xiaolong Wang
• 关键词：Protein remote homology ; Pseudo amino acid composition ; Support vector machine ; Principal component analysis
• 刊名：Molecular Genetics and Genomics
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：290
• 期：5
• 页码：1919-1931
• 全文大小：959 KB
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• 作者单位：Bin Liu (1) (2) (3)
  Junjie Chen (1)
  Xiaolong Wang (1) (2)
  
  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, 518055, Guangdong, People’s Republic of China
  2. Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, 518055, Guangdong, People’s Republic of China
  3. Gordon Life Science Institute, Belmont, MA, 02478, USA
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Life Sciences
  Cell Biology
  Biochemistry
  Microbial Genetics and Genomics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1617-4623

文摘

Protein remote homology detection is one of the important tasks in computational proteomics, which is important for basic research and practical application. Currently, the SVM-based discriminative methods have shown superior performance. However, the existing feature vectors still cannot suitably represent the protein sequences, and often lack an interpretable model for analysis of characteristic features. Previous studies showed that sequence-order effects and physicochemical properties are important for representing protein sequences. However, how to use these kinds of information for constructing predictors is still a challenging problem. In this study, in order to incorporate the sequence-order information and physicochemical properties into the prediction, a method called disPseAAC is proposed, in which the feature vector is constructed by combining the occurrences of amino acid pairs within the Chou’s pseudo amino acid composition (PseAAC) approach. The predictive performance and computational cost are further improved by employing the principal component analysis strategy. Various experiments are conducted on a benchmark dataset. Experimental results show that disPseAAC achieves an ROC score of 0.922, outperforming some existing state-of-the-art methods. Furthermore, the learnt model can easily be analyzed in terms of discriminative features, and the computational cost of the proposed method is much lower than that of other profile-based methods. Keywords Protein remote homology Pseudo amino acid composition Support vector machine Principal component analysis