Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

In diseases with a strong association with an HLA haplotype, identification of relevant T cell epitopes may allow alteration of the pathologic process. In this report we use a reverse immunogenetic approach to predict possible HLA class II-restricted T cell epitopes by using complete pool sequencing data. Data from HLA-DR2(B1*1501), -DR3(B1*0301), -DQ2(A1*0501, B1*0201), and -DQ8(A1*0301, B1*0302) alleles were used by a computer program that searches a candidate protein to predict ligands with a relatively high probability of being processed and presented. This approach successfully identified both known T cell epitopes and eluted single peptides from the parent protein. Furthermore, the program identified ligands from proteins in which the binding motif of the HLA molecule was unable to do so. When the information from the nonbinding N- and C-terminal regions in the pool sequence was removed, the ability to predict several ligands was markedly reduced, particularly for the HLA-DQ alleles. This suggests a possible role for these regions in determining ligands for HLA class II molecules. Thus, the use of complete eluted peptide sequence data offers a powerful approach to the prediction of HLA-DQ and -DR peptide ligands and T cell epitopes.

Type

Journal article

Journal

Journal of immunology (Baltimore, Md. : 1950)

Publication Date

07/1998

Volume

161

Pages

850 - 858

Addresses

Molecular Immunology Group, Institute of Molecular Medicine, John Radcliffe Hospital, Oxford, United Kingdom. agodkin@molbiol.ox.ac.uk

Keywords

Humans, Peptide Fragments, Gliadin, HLA-DQ Antigens, HLA-DR Antigens, Epitopes, T-Lymphocyte, Ligands, Peptide Mapping, Probability, Predictive Value of Tests, Antigen Presentation, Amino Acid Sequence, Protein Binding, Software, Molecular Sequence Data