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Predictor for the effect of amino acid composition on CD4+ T cell epitopes preprocessing.

ABSTRACT: Predictive tools for all levels of CD8+ T cell epitopes processing have reached a maturation level. Good prediction algorithms have been developed for proteasomal cleavage, TAP and MHC class I peptide binding. The same cannot be said of CD4+ T cell epitopes. While multiple algorithms of varying accuracy have been proposed for MHC class II peptide binding, the preprocessing of CD4+ T cell epitopes is still lacking a good prediction algorithm. CD4+ T cell epitopes generation includes several stages, not all which are well-defined. We here group these stages to produce a generic preprocessing stage predictor for the cleavage processes preceding the presentation of epitopes to CD4+ T cell. The predictor is learnt using a combination of in vitro cleavage experiments and observed naturally processed MHC class II binding peptides. The properties of the predictor highlight the effect of different factors on CD4+ T cell epitopes preprocessing. The most important factor emerging from the predictor is the secondary structure of the cleaved region in the protein. The effect of the secondary structure is expected since CD4+ T cell epitopes are not denatured before cleavage. A website developed based on this predictor is available at: http://peptibase.cs.biu.ac.il/PepCleave_cd4/.

SUBMITTER: Hoze E

PROVIDER: S-EPMC3646362 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

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Predictor for the effect of amino acid composition on CD4+ T cell epitopes preprocessing.

Hoze Ehud E Tsaban Lea L Maman Yaakov Y Louzoun Yoram Y

Journal of immunological methods 20130226 1-2

Predictive tools for all levels of CD8+ T cell epitopes processing have reached a maturation level. Good prediction algorithms have been developed for proteasomal cleavage, TAP and MHC class I peptide binding. The same cannot be said of CD4+ T cell epitopes. While multiple algorithms of varying accuracy have been proposed for MHC class II peptide binding, the preprocessing of CD4+ T cell epitopes is still lacking a good prediction algorithm. CD4+ T cell epitopes generation includes several stage ...[more]

PMID: 23481624

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Project description:BackgroundThe identification of immunogenic regions on the surface of antigens, which are able to be recognized by antibodies and to trigger an immune response, is a major challenge for the design of new and effective vaccines. The prediction of such regions through computational immunology techniques is a challenging goal, which will ultimately lead to a drastic limitation of the experimental tests required to validate their efficiency. However, current methods are far from being sufficiently reliable and/or applicable on a large scale.ResultsWe developed SEPIa, a B-cell epitope predictor from the protein sequence, which is sufficiently fast to be applicable on a large scale. The originality of SEPIa lies in the combination of two classifiers, a naïve Bayesian and a random forest classifier, through a voting algorithm that exploits the advantages of both. It is based on 13 sequence-based features, whose values in a 9-residue sequence window are compiled to predict the epitope/non-epitope state of the central residue. The features are related to the type of amino acid, its conservation in homologous proteins, and its tendency of being exposed to the solvent, soluble, flexible, and disordered. The highest signal is obtained from statistical amino acid preferences, but all 13 features contribute non-negligibly in the predictor. SEPIa's average prediction accuracy is limited, with an AUC score (area under the receiver operating characteristic curve) that reaches 0.65 both in 10-fold cross-validation and on an independent test set. It is nevertheless slightly higher than that of other methods evaluated on the same test set.ConclusionsSEPIa was applied to a test protein whose epitopes are known, human β2 adrenergic G-protein-coupled receptor, with promising results. Although the actual AUC score is rather low, many of the predicted epitopes cluster together and overlap the experimental epitope region. The reasons underlying the limitations of SEPIa and of all other B-cell epitope predictors are discussed.

Dataset Information

Predictor for the effect of amino acid composition on CD4+ T cell epitopes preprocessing.

Publications

Predictor for the effect of amino acid composition on CD4+ T cell epitopes preprocessing.

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