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

  • Andrew J. Bordner
Part of the Methods in Molecular Biology book series (MIMB, volume 1061)


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.

Key words

Peptide docking Molecular mechanics Machine learning Random Forest Binding affinity 



This work was supported by the Mayo Clinic.


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Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Andrew J. Bordner
    • 1
  1. 1.Mayo ClinicScottsdaleUSA

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