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Traditionally, T cell epitope discovery requires considerable amounts of tedious, slow, and costly experimental work. During the last decade, prediction tools have emerged as essential tools allowing researchers to select a manageable list of epitope candidates to test from a larger peptide, protein, or even proteome. However, no current tools address the complexity caused by the highly polymorphic nature of the restricting HLA molecules, which effectively individualizes T cell responses. To fill this gap, we here present an easy-to-use prediction tool named HLArestrictor ( http://www.cbs.dtu.dk/services/HLArestrictor ), which is based on the highly versatile and accurate NetMHCpan predictor, which here has been optimized for the identification of both the MHC restriction element and the corresponding minimal epitope of a T cell response in a given individual. As input, it requires high-resolution (i.e., 4-digit) HLA typing of the individual. HLArestrictor then predicts all 8-11mer peptide binders within one or more larger peptides and provides an overview of the predicted HLA restrictions and minimal epitopes. The method was tested on a large dataset of HIV IFNγ ELIspot peptide responses and was shown to identify HLA restrictions and minimal epitopes for about 90% of the positive peptide/patient pairs while rejecting more than 95% of the negative peptide-HLA pairs. Furthermore, for 18 peptide/HLA tetramer validated responses, HLArestrictor in all cases predicted both the HLA restriction element and minimal epitope. Thus, HLArestrictor should be a valuable tool in any T cell epitope discovery process aimed at identifying new epitopes from infectious diseases and other disease models.

Original publication

DOI

10.1007/s00251-010-0493-5

Type

Journal article

Journal

Immunogenetics

Publication Date

01/2011

Volume

63

Pages

43 - 55

Keywords

Amino Acid Sequence, CD8-Positive T-Lymphocytes, Cell Line, Cohort Studies, Databases, Protein, Enzyme-Linked Immunospot Assay, Epitope Mapping, Epitopes, T-Lymphocyte, HIV Infections, HLA Antigens, Humans, Interferon-gamma, Molecular Sequence Data, Peptides, Software