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Prediction of protein amidation sites by feature selection and analysis

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Abstract

Carboxy-terminal α-amidation is a widespread post-translational modification of proteins found widely in vertebrates and invertebrates. The α-amide group is required for full biological activity, since it may render a peptide more hydrophobic and thus better be able to bind to other proteins, preventing ionization of the C-terminus. However, in particular, the C-terminal amidation is very difficult to detect because experimental methods are often labor-intensive, time-consuming and expensive. Therefore, in silico methods may complement due to their high efficiency. In this study, a computational method was developed to predict protein amidation sites, by incorporating the maximum relevance minimum redundancy method and the incremental feature selection method based on the nearest neighbor algorithm. From a total of 735 features, 41 optimal features were selected and were utilized to construct the final predictor. As a result, the predictor achieved an overall Matthews correlation coefficient of 0.8308. Feature analysis showed that PSSM conservation scores and amino acid factors played the most important roles in the α-amidation site prediction. Site-specific feature analyses showed that features derived from the amidation site itself and adjacent sites were most significant. This method presented could be used as an efficient tool to theoretically predict amidated peptides. And the selected features from our study could shed some light on the in-depth understanding of the mechanisms of the amidation modification, providing guidelines for experimental validation.

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Acknowledgments

This work was supported by grants from National Basic Research Program of China (2011CB510102, 2011CB510101), National Science Foundation of China (81171342, 81201148), Innovation Program of Shanghai Municipal Education Commission (12ZZ087), Independent Innovation Foundation of Tianjin University (60302069, 60302064), The National Research Foundation for the Doctoral Program of Higher Education of China (20120032120073) and the grant of “The First-class Discipline of Universities in Shanghai”.

Conflict of interest

The authors declare no competing interests.

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Authors and Affiliations

  1. Institute of Systems Biology, Shanghai University, Shanghai, 200444, People’s Republic of China

    Lele Hu & Yudong Cai

  2. CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Weiren Cui

  3. Department of Mathematics, College of Science, Shanghai University, Shanghai, China

    Yudong Cai

  4. Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People’s Republic of China

    Shen Niu, Tao Huang & Yixue Li

  5. Shanghai Center for Bioinformation Technology, Shanghai, 200235, People’s Republic of China

    Shen Niu, Lulu Zheng, Tao Huang, Kaiyan Feng & Yixue Li

  6. Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, Wuhan, 430074, China

    Lulu Zheng

  7. Division of Theoretical Bioinformatics (BO80), German Cancer Research Center, 69120, Heidelberg, Germany

    Lei Gu

  8. Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin, 300072, People’s Republic of China

    Ning Zhang

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  1. Weiren Cui
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  2. Shen Niu
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  3. Lulu Zheng
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  8. Ning Zhang
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  9. Yudong Cai
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Corresponding authors

Correspondence to Kaiyan Feng, Ning Zhang, Yudong Cai or Yixue Li.

Additional information

Communicated by S. Hohmann.

W. Cui, S. Niu and L. Zheng contributed equally to this work.

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Cui, W., Niu, S., Zheng, L. et al. Prediction of protein amidation sites by feature selection and analysis. Mol Genet Genomics 288, 391–400 (2013). https://doi.org/10.1007/s00438-013-0760-x

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