Properties of MHC Class I Presented Peptides That Enhance Immunogenicity

<div><p>T-cells have to recognize peptides presented on MHC molecules to be activated and elicit their effector functions. Several studies demonstrate that some peptides are more immunogenic than others and therefore more likely to be T-cell epitopes. We set out to determine which properties cause such differences in immunogenicity. To this end, we collected and analyzed a large set of data describing the immunogenicity of peptides presented on various MHC-I molecules. Two main conclusions could be drawn from this analysis: First, in line with previous observations, we showed that positions P4–6 of a presented peptide are more important for immunogenicity. Second, some amino acids, especially those with large and aromatic side chains, are associated with immunogenicity. This information was combined into a simple model that was used to demonstrate that immunogenicity is, to a certain extent, predictable. This model (made available at <a href="http://tools.iedb.org/immunogenicity/" target="_blank">http://tools.iedb.org/immunogenicity/</a>) was validated with data from two independent epitope discovery studies. Interestingly, with this model we could show that T-cells are equipped to better recognize viral than human (self) peptides. After the past successful elucidation of different steps in the MHC-I presentation pathway, the identification of variables that influence immunogenicity will be an important next step in the investigation of T-cell epitopes and our understanding of cellular immune responses.</p></div

T-cell preferences for different amino acids in HLA class I presented peptides.

<p>The fraction of an amino acid in immunogenic (left bar, filled) and non-immunogenic (right bar, unfilled) peptides presented on HLA class I molecules is shown. Significantly different distributions are indicated with a star (Permutation test, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#s4" target="_blank">Methods</a>: p<0.05; False discovery rate (FDR) for multiple testing determined as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#pcbi.1003266-Storey1" target="_blank">[46]</a>: q<0.05). The background frequency for each amino acid in the protein sequences that were a source of the immunogenic or non-immunogenic peptides is shown by a grey line.</p

Cross-validation of the immunogenicity model.

<p>Two-thirds of the data were used for making the immunogenicity model (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#s4" target="_blank">methods</a>) and one-third for cross-validation. The average ROC (thick grey line) of 25 of such cross-validations (thin lines) are plotted. The average AUC was 0.65.</p

Amino acid characteristics of immunogenic peptides presented on HLA class I molecules.

<p>Sets of amino acids were counted in immunogenic and non-immunogenic peptides based on size, aromaticity, acidity and charge, and enrichments were determined (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#s4" target="_blank">Methods</a>). The association of these characteristics (e.g. size) with immunogenicity was tested by comparing the distributions in one extreme of a characteristic with the distribution in the other extreme of that characteristic (e.g. large versus small) using Fisher's exact test. This way, one test is performed per characteristic.</p

Data acquisition and handling oversight.

<p>Data was collected from four different sources (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#s4" target="_blank">Methods</a>). The first panel shows how many pMHCs were derived from each data set and their respective MHC restrictions and immunogenicity status. Data from all sets was combined, the number of non-redundant 9mers with respect to the host in which the data was obtained is shown in the second panel.</p

image_1_Predicting HLA CD4 Immunogenicity in Human Populations.PDF

Background<p>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.</p>Methods<p>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.</p>Results<p>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).</p>Conclusion<p>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.</p

table_2_Predicting HLA CD4 Immunogenicity in Human Populations.xlsx

Background<p>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.</p>Methods<p>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.</p>Results<p>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).</p>Conclusion<p>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.</p

table_4_Predicting HLA CD4 Immunogenicity in Human Populations.xlsx

Background<p>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.</p>Methods<p>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.</p>Results<p>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).</p>Conclusion<p>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.</p

table_3_Predicting HLA CD4 Immunogenicity in Human Populations.xlsx

Background<p>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.</p>Methods<p>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.</p>Results<p>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).</p>Conclusion<p>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.</p

Differential polarization of T cell responses as a function of the original vaccine type used for childhood vaccination.

<p>IFNγ and IL-5 responses were measured by dual color ELISPOT assays. Each data point represents the ratio of IFNγ/IL-5 responses of each positive individual peptide from all the reactive donors. Median ± interquartile range for donors originally primed with DTwP or DTaP vaccine is represented. Two-tailed Mann-Whitney test.</p