Learning MHC I—peptide binding
Bioinformatics
Predictive vaccinology: optimisation of predictions using support vector machine classifiers
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
A hybrid model for prediction of peptide binding to MHC molecules
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
The immunogrid simulator: how to use it
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
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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.