Structure-based prediction of major histocompatibility complex (MHC) epitopes

Andrew J. Bordner

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationImmunoproteomics
Subtitle of host publicationMethods and Protocols
EditorsKelly Fulton, Susan Twine
Pages323-343
Number of pages21
DOIs
StatePublished - Sep 20 2013

Publication series

NameMethods in Molecular Biology
Volume1061
ISSN (Print)1064-3745

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Keywords

  • Binding affinity
  • Machine learning
  • Molecular mechanics
  • Peptide docking
  • Random Forest

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

Cite this

Bordner, A. J. (2013). Structure-based prediction of major histocompatibility complex (MHC) epitopes. In K. Fulton, & S. Twine (Eds.), Immunoproteomics: Methods and Protocols (pp. 323-343). (Methods in Molecular Biology; Vol. 1061). https://doi.org/10.1007/978-1-62703-589-7_20