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Computational Prediction of Protein O-GlcNAc Modification

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Computational Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1754))

Abstract

Protein O-GlcNAcylation on serine and threonine residues is a significant posttranslational modification. Experimental techniques can uncover only a small portion of O-GlcNAcylation sites. Several computational algorithms have been proposed as necessary auxiliary tools to identify potential O-GlcNAcylation sites. This chapter discusses the metrics and procedures used to assess prediction tools and surveys six computational tools for the prediction of protein O-GlcNAcylation sites. Analyses of these tools using an independent test dataset indicated the advantages and disadvantages of the six existing prediction methods. We also discuss the challenges that may be faced while developing novel predictors in the future.

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Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (3132016306, 3132017048).

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Authors and Affiliations

  1. Department of Mathematics, Dalian Maritime University, Dalian, China

    Cangzhi Jia & Yun Zuo

Authors
  1. Cangzhi Jia
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  2. Yun Zuo
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Corresponding author

Correspondence to Cangzhi Jia .

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Editors and Affiliations

  1. Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Tao Huang

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Jia, C., Zuo, Y. (2018). Computational Prediction of Protein O-GlcNAc Modification. In: Huang, T. (eds) Computational Systems Biology. Methods in Molecular Biology, vol 1754. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7717-8_14

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  • DOI: https://doi.org/10.1007/978-1-4939-7717-8_14

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7716-1

  • Online ISBN: 978-1-4939-7717-8

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