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In Silico Prediction of Post-translational Modifications

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In Silico Tools for Gene Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 760))

Abstract

Methods for predicting protein post-translational modifications have been developed extensively. In this chapter, we review major post-translational modification prediction strategies, with a particular focus on statistical and machine learning approaches. We present the workflow of the methods and summarize the advantages and disadvantages of the methods.

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Authors and Affiliations

  1. Department of Systems and Computer Science, Howard University, Washington, DC, USA

    Chunmei Liu & Hui Li

Authors
  1. Chunmei Liu
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  2. Hui Li
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Correspondence to Chunmei Liu .

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Editors and Affiliations

  1. Royal Prince Alfred Hospital, Dept. of Molecular & Clinical Genetics, University of Sydney, Missenden Road, Camperdown, 2050, New South Wales, Australia

    Bing Yu

  2. Royal Prince Alfred Hospital, Dept. Molecular & Clinical Genetics, University of Sydney, Missenden Road, Camperdown, 2050, New South Wales, Australia

    Marcus Hinchcliffe

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Liu, C., Li, H. (2011). In Silico Prediction of Post-translational Modifications. In: Yu, B., Hinchcliffe, M. (eds) In Silico Tools for Gene Discovery. Methods in Molecular Biology, vol 760. Humana Press. https://doi.org/10.1007/978-1-61779-176-5_20

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  • DOI: https://doi.org/10.1007/978-1-61779-176-5_20

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  • Print ISBN: 978-1-61779-175-8

  • Online ISBN: 978-1-61779-176-5

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