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On using physico-chemical properties of amino acids in string kernels for protein classification via support vector machines

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Abstract

String kernels are popular tools for analyzing protein sequence data and they have been successfully applied to many computational biology problems. The traditional string kernels assume that different substrings are independent. However, substrings can be highly correlated due to their substructure relationship or common physico-chemical properties. This paper proposes two kinds of weighted spectrum kernels: The correlation spectrum kernel and the AA spectrum kernel. We evaluate their performances by predicting glycan-binding proteins of 12 glycans. The results show that the correlation spectrum kernel and the AA spectrum kernel perform significantly better than the spectrum kernel for nearly all the 12 glycans. By comparing the predictive power of AA spectrum kernels constructed by different physico-chemical properties, the authors can also identify the physicochemical properties which contributes the most to the glycan-protein binding. The results indicate that physico-chemical properties of amino acids in proteins play an important role in the mechanism of glycan-protein binding.

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Author information

Authors and Affiliations

  1. Institute of Information and System Science, Xi’an Jiaotong University, Xi’an, 710049, China

    Limin Li

  2. Department of Bioinformatics, Faculty of Engineering, Soka University, Tokyo, Japan

    Kiyoko F. Aoki-Kinoshita

  3. Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong, China

    Wai-Ki Ching

  4. Department of Mathematics, School of Information, Renmin University of China, Beijing, 100872, China

    Hao Jiang

Authors
  1. Limin Li
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  2. Kiyoko F. Aoki-Kinoshita
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  3. Wai-Ki Ching
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  4. Hao Jiang
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Corresponding author

Correspondence to Hao Jiang.

Additional information

This research was supported in part by Research Grants Council of Hong Kong under Grant No. 17301214 and HKU CERG Grants and Hung Hing Ying Physical Research Grant, and the Research Funds of Renmin University of China, and the National Natural Science Foundation of China under Grant Nos. 11271144, 11101382, 11471256, and S201201009985.

This paper was recommended for publication by Editor ZOU Guohua.

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Li, L., Aoki-Kinoshita, K.F., Ching, WK. et al. On using physico-chemical properties of amino acids in string kernels for protein classification via support vector machines. J Syst Sci Complex 28, 504–516 (2015). https://doi.org/10.1007/s11424-015-2156-y

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  • DOI: https://doi.org/10.1007/s11424-015-2156-y

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