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Empirical, AI, and QSAR Approaches to Peptide-MHC Binding Prediction

  • Channa K Hattotuwagama
  • Pingping Guan
  • Matthew Davies
  • Debra J Taylor
  • Valerie Walshe
  • Shelley L Hemsley
  • Christopher Toseland
  • Irini A Doytchinova
  • Persephone Borrow
  • Darren R Flower

Abstract

Immunoinformatics is facilitating important change within immunology and is helping it to engage more completely with the dynamic post-genomic revolution sweeping through bioscience. Historically, predicting the specificity of peptide Major Histocompatibility Complex (MHC) interactions has been the major contribution made by bioinformatics disciplines to research in immunology and the vaccinology. This will be the focus of the current chapter. Initially, we will review some background to this problem, such as the thermodynamics of peptide binding and the known constraints on peptide selectivity by the MHC. We will then review artificial intelligence and machine learning approaches to the prediction problem. Finally, we will outline our own contribution to this field: the application of QSAR techniques to the prediction of peptide-MHC binding.

Keywords

Partial Little Square Peptide Binding Anchor Residue Anchor Position Peptide Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Channa K Hattotuwagama
    • 1
  • Pingping Guan
    • 1
  • Matthew Davies
    • 1
  • Debra J Taylor
    • 1
  • Valerie Walshe
    • 1
  • Shelley L Hemsley
    • 1
  • Christopher Toseland
    • 1
  • Irini A Doytchinova
    • 1
  • Persephone Borrow
    • 1
  • Darren R Flower
    • 1
  1. 1.The Jenner InstituteUniversity of OxfordCompton, BerkshireUK

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