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

Hongjin Bian, John F. Reidhaar-Olson, Juergen Hammer

Research output: Contribution to journalArticle

64 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)299-309
Number of pages11
JournalMethods
Volume29
Issue number3
DOIs
StatePublished - Mar 1 2003

Keywords

  • Epitope prediction
  • Human leukocyte antigen class II
  • TEPITOPE
  • Vaccinome

ASJC Scopus subject areas

  • Molecular Biology
  • Biochemistry, Genetics and Molecular Biology(all)

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