The use of bioinformatics for identifying class II-restricted T-cell epitopes

An important step in the design of subunit vaccines is the identification of promiscuous T helper cell epitopes in sets of disease-specific gene products. Most of the epitope prediction models are based on HLA-II peptide binding, which constitutes a major bottleneck in the natural selection of epito...

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Bibliographic Details
Published in:Methods (San Diego, Calif.) Calif.), 2003-03, Vol.29 (3), p.299-309
Main Authors: Bian, Hongjin, Reidhaar-Olson, John F., Hammer, Juergen
Format: Article
Language:English
Subjects:
Computational Biology - methods
Epitope Mapping - methods
Epitope prediction
Epitopes, T-Lymphocyte - analysis
Histocompatibility Antigens Class II - analysis
Human leukocyte antigen class II
Humans
TEPITOPE
Vaccines
Vaccinome
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Summary:An important step in the design of subunit vaccines is the identification of promiscuous T helper cell epitopes in sets of disease-specific gene products. Most of the epitope prediction models are based on HLA-II peptide binding, which constitutes a major bottleneck in the natural selection of epitopes. Here we describe a computer model, TEPITOPE, that enables the systematic prediction of promiscuous peptide ligands for a broad range of HLA binding specificity. We show how to apply the TEPITOPE prediction model to identify T-cell epitopes, and provide examples of its successful application in the context of oncology, allergy, and infectious and autoimmune diseases.
ISSN:1046-2023
1095-9130
DOI:10.1016/S1046-2023(02)00352-3