Development and validation of a broad scheme for prediction of HLA class II restricted T cell epitopes

Abstract

Computational prediction of HLA class II restricted T cell epitopes has great significance in many immunological studies including vaccine discovery. In recent years, prediction of HLA class II binding has improved significantly but a strategy to globally predict the most dominant epitopes has not been rigorously defined. Using human immunogenicity data associated with sets of 15-mer peptides overlapping by 10 residues spanning over 30 different allergens and bacterial antigens, and HLA class II binding prediction tools from the Immune Epitope Database and Analysis Resource (IEDB), we optimized a strategy to predict the top epitopes recognized by human populations. The most effective strategy was to select peptides based on predicted median binding percentiles for a set of seven DRB1 and DRB3/4/5 alleles. These results were validated with predictions on a blind set of 15 new allergens and bacterial antigens. We found that the top 21% predicted peptides (based on the predicted binding to seven DRB1 and DRB3/4/5 alleles) were required to capture 50% of the immune response. This corresponded to an IEDB consensus percentile rank of 20.0, which could be used as a universal prediction threshold. Utilizing actual binding data (as opposed to predicted binding data) did not appreciably change the efficacy of global predictions, suggesting that the imperfect predictive capacity is not due to poor algorithm performance, but intrinsic limitations of HLA class II epitope prediction schema based on HLA binding in genetically diverse human populations.

HLA-DRB1 Alleles Are Associated With Different Magnitudes of Dengue Virus–Specific CD4+T-Cell Responses

“…Third, other less common DR types, as well as DP and DQ molecules, will have to be addressed in future studies. Based on the available knowledge, predictions for the main DRB1 molecules will cover approximately 50% of the total response [22,23]. We plan to perform additional epitope identification studies addressing other HLA class II molecules, continue to update our mega-pool, and provide the epitope sequences by submission to the Immune epitope database (available at: http://www.iedb.org) [24].…”

Poor correlation between T-cell activation assays and HLA-DR binding prediction algorithms in an immunogenic fragment of Pseudomonas exotoxin A

“…Moreover, focus on DR binding prediction is considered highly common in the art of immunogenicity prediction (Iwai et al, 2003; Tangri et al, 2005; Koren et al, 2007; Cantor et al, 2011; Abdel-Hady et al, 2014; King et al, 2014; Li et al, 2014; Salvat et al, 2014; Salvat et al, 2015) and it has been recently shown that prediction of DP and DQ had very low correlation with experimental data. (Paul et al, 2015) Therefore, we chose to focus only on DR. In our comparison study we found four epitopes that were missed by the prediction algorithm (FN) in most thresholds.…”

“…When we had completed our study, an analysis comparing the computed and experimental responses of more than 95 donors and peptides derived from 30 proteins was published (Paul et al, 2015). This work focused solely on the false positive rate of the predictions, mainly due to the nature of the experimental work that did not allow identification of false negative predicted peptides.…”

Design and engineering of deimmunized biotherapeutics

“…[59] Thus, the highly polymorphic nature of MHC II alleles can be rendered more tractable by predicting for alleles that are both commonly encoded and broadly representative of key MHC II supertypes. [59,60] Additionally, experimental evidence suggests that immunodominant epitopes are those that bind multiple MHC II alleles, and therefore computational predictions can be further refined by searching for high risk “promiscuous” MHC II binders. [26,61] T cell epitope databases and prediction algorithms are regularly updated and improved,[62] and there exist codified strategies for employing these epitope predictors to guide immunogenicity risk assessment and protein deimmunization.…”