Chapter 5. Vaccines: Data Driven Prediction of Binders, Epitopes and Immunogenicity

  1. Darren R Flower

Published Online: 24 DEC 2009

DOI: 10.1002/9780470699836.ch5

Bioinformatics for Vaccinology

Bioinformatics for Vaccinology

How to Cite

Flower, D. R. (2008) Vaccines: Data Driven Prediction of Binders, Epitopes and Immunogenicity, in Bioinformatics for Vaccinology, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470699836.ch5

Author Information

  1. Edward Jenner Institute for Vaccine Research, Compton, Berkshire, UK

Publication History

  1. Published Online: 24 DEC 2009
  2. Published Print: 7 NOV 2008

ISBN Information

Print ISBN: 9780470027110

Online ISBN: 9780470699836

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Keywords:

  • vaccines - binders, epitopes, immunogenicity;
  • epitope prediction and peptide–MHC binding prediction;
  • modern measurement methods;
  • MHC peptide-specificity and dominant anchor positions;
  • artificial neural networks (ANNs);
  • Partial least squares (PLS) regression;
  • quantitative structure activity relationships (QSAR);
  • principal component analysis (PCA) and amino acid scales;
  • prediction accuracy assessment

Summary

This chapter contains sections titled:

  • Towards epitope-based vaccines

  • T cell epitope prediction

  • Predicting MHC binding

  • Binding is biology

  • Quantifying binding

  • Entropy, enthalpy and entropy-enthalpy compensation

  • Experimental measurement of binding

  • Modern measurement methods

  • Isothermal titration calorimetry

  • Long and short of peptide binding

  • The class I peptide repertoire

  • Practicalities of binding prediction

  • Binding becomes recognition

  • Immunoinformatics lends a hand

  • Motif based prediction

  • The imperfect motif

  • Other approaches to binding prediction

  • Representing sequences

  • Computer science lends a hand

  • Artificial neural networks

  • Hidden Markov models

  • Support vector machines

  • Robust multivariate statistics

  • Partial least squares

  • Quantitative structure activity relationships

  • Other techniques and sequence representations

  • Amino acid properties

  • Direct epitope prediction

  • Predicting antigen presentation

  • Predicting class II MHC binding

  • Assessing prediction accuracy

  • ROC plots

  • Quantitative accuracy

  • Prediction assessment protocols

  • Comparing predictions

  • Prediction versus experiment

  • Predicting B cell epitopes

  • Peak profiles and smoothing

  • Early methods

  • Imperfect B cell prediction

  • References