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

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Abstract

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.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61300112 and 61272383), the Scientific Research Innovation Foundation in Harbin Institute of Technology (Project No. HIT.NSRIF.2013103), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, the Natural Science Foundation of Guangdong Province (2014A030313695), and Shenzhen Municipal Science and Technology Innovation Council (Grant No. CXZZ20140904154910774).

Conflict of interest

The authors declare that they have no competing interests.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Authors and Affiliations

  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

    Bin Liu, Junjie Chen & Xiaolong Wang

  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

    Bin Liu & Xiaolong Wang

  3. Gordon Life Science Institute, Belmont, MA, 02478, USA

    Bin Liu

Authors
  1. Bin Liu
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  2. Junjie Chen
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  3. Xiaolong Wang
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Correspondence to Bin Liu.

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Communicated by S. Hohmann.

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Liu, B., Chen, J. & Wang, X. Protein remote homology detection by combining Chou’s distance-pair pseudo amino acid composition and principal component analysis. Mol Genet Genomics 290, 1919–1931 (2015). https://doi.org/10.1007/s00438-015-1044-4

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  • DOI: https://doi.org/10.1007/s00438-015-1044-4

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