Sinu Paul
ABSTRACT
Computational prediction of HLA class II restricted T cell epitopes has great significance in many immunological studies including vaccine discovery. With the development of novel bioinformatics approaches, prediction of HLA class II binding has improved significantly but a strategy to predict the most dominant HLA class II epitopes has not been defined. Using different sets of peptides from various allergen and bacterial antigens and HLA class II binding prediction tools from the IEDB, we have designed a strategy to predict the top epitopes from any antigen. We found that the top 21% of 15-mer peptides overlapping by 10 residues (based on the predicted binding to seven DRB1 and DRB3/4/5 alleles) capture 50% of the immune response. This corresponded to an IEDB consensus percentile rank of 19.82 which could be used as a universal prediction threshold.
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Index Terms
- Development and validation of a broad scheme for prediction of HLA class II restricted T cell epitopes
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