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Structure-Based Prediction of Major Histocompatibility Complex (MHC) Epitopes

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Immunoproteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1061))

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Abstract

Because of the enormous diversity of both MHC proteins and peptide epitopes, computational epitope prediction methods are needed in order to supplement limited experimental data. These prediction methods are useful for guiding experiments and have many potential biomedical applications. Unlike popular sequence-based methods, structure-based epitope prediction methods can predict epitopes for multiple MHC types with highly distinct peptide binding propensities. In this chapter, we describe in detail our previously developed structure-based epitope prediction methods for both class I and class II MHC proteins. We also discuss the relative advantages and disadvantages of sequence-based versus structure-based methods and how to evaluate prediction performance.

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Acknowledgment

This work was supported by the Mayo Clinic.

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Authors and Affiliations

  1. Mayo Clinic, Scottsdale, AZ, USA

    Andrew J. Bordner

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  1. Andrew J. Bordner
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Editors and Affiliations

  1. National Research Council Canada Human Health Therapeutics, Ottawa, Ontario, Canada

    Kelly M. Fulton

  2. National Research Council Canada Human Health Therapeutics, Ottawa, Ontario, Canada

    Susan M. Twine

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Bordner, A.J. (2013). Structure-Based Prediction of Major Histocompatibility Complex (MHC) Epitopes. In: Fulton, K., Twine, S. (eds) Immunoproteomics. Methods in Molecular Biology, vol 1061. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-589-7_20

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  • DOI: https://doi.org/10.1007/978-1-62703-589-7_20

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-588-0

  • Online ISBN: 978-1-62703-589-7

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