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Presentation
Understanding prediction systems for HLA-binding peptides and T-cell epitope identification
Paper presented at the International workshop on pattern recognition in bioinformatics (2007)
  • L. You
  • P. Zhang
  • M. Boden
  • V. Brusic
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 surviving 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.

© Copyright Springer-Verlag Berlin Heidelberg, 2007

Access the published version at http://www.springerlink.com/.

Keywords
  • HLA-binding peptides,
  • T-cell epitope identification
Publication Date
October 2, 2007
Citation Information
L. You, P. Zhang, M. Boden and V. Brusic. "Understanding prediction systems for HLA-binding peptides and T-cell epitope identification" Paper presented at the International workshop on pattern recognition in bioinformatics (2007)
Available at: http://works.bepress.com/ping_zhang/9/