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Immunoinformatics pp 197–215Cite as

Computational Prediction of B Cell Epitopes from Antigen Sequences

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1184))

Abstract

Computational identification of B-cell epitopes from antigen chains is a difficult and actively pursued research topic. Efforts towards the development of method for the prediction of linear epitopes span over the last three decades, while only recently several predictors of conformational epitopes were released. We review a comprehensive set of 13 recent approaches that predict linear and 4 methods that predict conformational B-cell epitopes from the antigen sequences. We introduce several databases of B-cell epitopes, since the availability of the corresponding data is at the heart of the development and validation of computational predictors. We also offer practical insights concerning the use and availability of these B-cell epitope predictors, and motivate and discuss feature research in this area.

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Acknowledgements

This work was supported by National Science Foundation of China (NSFC) grants 31050110432 and 31150110577 to L.K. J.G. was supported by the Fundamental Research Funds for the Central Universities grant 65011491.

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Authors and Affiliations

  1. School of Mathematical Sciences, Nankai University, Tianjin, People’s Republic of China

    Jianzhao Gao

  2. Department of Electrical and Computer Engineering, University of Alberta, ECERF, 2nd Floor, 9107 116th Street, Edmonton, AB, T6G 2V4, Canada

    Lukasz Kurgan

Authors
  1. Jianzhao Gao
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  2. Lukasz Kurgan
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Correspondence to Lukasz Kurgan .

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Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, West Bengal, India

    Rajat K. De

  2. Indian Statistical Institute, Kolkata, West Bengal, India

    Namrata Tomar

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Gao, J., Kurgan, L. (2014). Computational Prediction of B Cell Epitopes from Antigen Sequences. In: De, R., Tomar, N. (eds) Immunoinformatics. Methods in Molecular Biology, vol 1184. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1115-8_11

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  • DOI: https://doi.org/10.1007/978-1-4939-1115-8_11

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  • Print ISBN: 978-1-4939-1114-1

  • Online ISBN: 978-1-4939-1115-8

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