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

Methods. 2003 Mar;29(3):299-309. doi: 10.1016/s1046-2023(02)00352-3.


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.

Publication types

  • Review

MeSH terms

  • Computational Biology / methods*
  • Epitope Mapping / methods*
  • Epitopes, T-Lymphocyte / analysis*
  • Histocompatibility Antigens Class II / analysis*
  • Humans
  • Vaccines


  • Epitopes, T-Lymphocyte
  • Histocompatibility Antigens Class II
  • Vaccines