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 language | English (US) |
---|---|
Pages (from-to) | 299-309 |
Number of pages | 11 |
Journal | Methods |
Volume | 29 |
Issue number | 3 |
DOIs | |
State | Published - Mar 1 2003 |
Externally published | Yes |
Keywords
- Epitope prediction
- Human leukocyte antigen class II
- TEPITOPE
- Vaccinome
ASJC Scopus subject areas
- Molecular Biology
- General Biochemistry, Genetics and Molecular Biology