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. 2018 Jun 14;9:1369.
doi: 10.3389/fimmu.2018.01369. eCollection 2018.

Predicting HLA CD4 Immunogenicity in Human Populations

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Free PMC article

Predicting HLA CD4 Immunogenicity in Human Populations

Sandeep Kumar Dhanda et al. Front Immunol. .
Free PMC article

Abstract

Background: Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides.

Methods: Sets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an "immunogenicity score." We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level.

Results: The immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore).

Conclusion: The new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification.

Keywords: HLA; TCR repertoire; bioinformatics; epitopes; immunodominance; immunogenicity; predictions.

Figures

Figure 1
Figure 1
Predictive performances for different motif lengths. Bars show cross-validation performance for the training dataset. Area under the ROC curve (AUC) values are shown for each artificial neural network training done by choosing different sequence lengths to define a preferred sequence motif within a 15-mer peptide. Error bars show SD of the five cross-validation sets.
Figure 2
Figure 2
Predictive performances obtained combining HLA binding and immunogenicity scores. The figure shows the performance dependency on an α coefficient used to combine HLA binding and immunogenicity scores. The model trained on the training dataset described in the text and validated on independent literature datasets, also described in the text.
Figure 3
Figure 3
Performance of independent literature datasets with combined approach and varying degree of alpha on the model trained with initial, in-house and tetramer datasets. The prediction values from HLA score and immunogencity score using different values of alpha are shown. A cutoff of 0.4 value for alpha is also highlighted by a dotted line.
Figure 4
Figure 4
Two-sample logo created using epitopes and non-epitopes in all the data (p-value < 0.01). The immunogenicity motifs for epitopes and non-epitopes were derived from the combination of all the datasets.
Figure 5
Figure 5
Screenshot for home page of immunogenicity prediction server.

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