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Understanding Prediction Systems for HLA-Binding Peptides and T-Cell Epitope Identification

  • Liwen You
  • Ping Zhang
  • Mikael Bodén
  • Vladimir Brusic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)

Abstract

Peptide binding to HLA molecules is a critical step in induction and regulation of T-cell mediated immune responses. Because of combinatorial complexity of immune responses, systematic studies require combination of computational methods and experimentation. Most of available computational predictions are based on discriminating binders from non-binders based on use of suitable prediction thresholds. We compared four state-of-the-art binding affinity prediction models and found that nonlinear models show better performance than linear models. A comprehensive analysis of HLA binders (A*0101, A*0201, A*0301, A*1101, A*2402, B*0702, B*0801 and B*1501) showed that non-linear predictors predict peptide binding affinity with high accuracy. The analysis of known T-cell epitopes of survivin and known HIV T-cell epitopes showed lack of correlation between binding affinity and immunogenicity of HLA-presented peptides. T-cell epitopes, therefore, can not be directly determined from binding affinities by simple selection of the highest affinity binders.

Keywords

Major Histocompatibility Complex Human Leukocyte Antigen Peptide Binding Human Leukocyte Antigen Class Human Leukocyte Antigen Allele 
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-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Liwen You
    • 1
    • 2
    • 3
  • Ping Zhang
    • 3
  • Mikael Bodén
    • 4
  • Vladimir Brusic
    • 3
    • 5
  1. 1.School of Information Science, Computer and Electrical Engineering, Halmstad University, HalmstadSweden
  2. 2.Department of Theoretical Physics, Lund University, LundSweden
  3. 3.School of Land, Crop, and Food Sciences, and University of Queensland, Brisbane QLDAustralia
  4. 4.School of Information Technology and Electrical Engineering, University of Queensland, Brisbane QLDAustralia
  5. 5.Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston MAUSA

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