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 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.
Bian
Hongjin
H
Section of Bioinformatics, Genetics and Genomics, Hoffmann-La Roche Inc., 340 Kingsland Street, Nutley, NJ 07110-1199, USA. hongjin.bian@roche.com
Reidhaar-Olson
John F
JF
Hammer
Juergen
J
D019295:Computational Biology / Q000379:methods*; D018604:Epitope Mapping / Q000379:methods*; D018984:Epitopes, T-Lymphocyte / Q000032:analysis*; D000949:Histocompatibility Antigens Class II / Q000032:analysis*; D006801:Humans; D014612:Vaccines
D018984:Epitopes, T-Lymphocyte; D000949:Histocompatibility Antigens Class II; D014612:Vaccines