Advanced

Understanding prediction systems for HLA-binding peptides and T-cell epitope identification

You, Liwen LU ; Zhang, Ping; Bodén, Mikael and Brusic, Vladimir (2007) In Lecture Notes in Bioinformatics 4774/2007(4774). p.337-348
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Lecture Notes in Bioinformatics
volume
4774/2007
issue
4774
pages
337 - 348
publisher
Springer
external identifiers
  • scopus:38349072025
ISSN
1611-3349
0302-9743
ISBN
978-3-540-75285-1
DOI
10.1007/978-3-540-75286-8_32
language
English
LU publication?
yes
id
5137a996-6c17-4b19-91cc-33fbac7c3662 (old id 796685)
date added to LUP
2007-12-28 14:15:23
date last changed
2017-06-11 03:48:26
@inbook{5137a996-6c17-4b19-91cc-33fbac7c3662,
  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.},
  author       = {You, Liwen and Zhang, Ping and Bodén, Mikael and Brusic, Vladimir},
  isbn         = {978-3-540-75285-1},
  issn         = {1611-3349},
  language     = {eng},
  number       = {4774},
  pages        = {337--348},
  publisher    = {Springer},
  series       = {Lecture Notes in Bioinformatics},
  title        = {Understanding prediction systems for HLA-binding peptides and T-cell epitope identification},
  url          = {http://dx.doi.org/10.1007/978-3-540-75286-8_32},
  volume       = {4774/2007},
  year         = {2007},
}