title: Discovery of promiscuous HLA-II-restricted T cell epitopes with TEPITOPE.
abstract: TEPITOPE is a prediction model that has been successfully applied to the in silico identification of T cell epitopes in the context of oncology, allergy, infectious diseases, and autoimmune diseases. Like most epitope prediction models, TEPITOPE's underlying algorithm is based on the prediction of HLA-II peptide binding, which constitutes a major bottleneck in the natural selection of epitopes. An important step in the design of subunit vaccines is the identification of promiscuous HLA-II ligands in sets of disease-specific gene products. TEPITOPE's user interface 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 both a road map and examples of its successful application.
authors.affiliation: Section of Bioinformatics, Genetics and Genomics, Hoffmann-La Roche Inc., Nutley, New Jersey, USA.
authors.forename: Hongjin
authors.initials: H
authors.lastname: Bian
authors.forename: Juergen
authors.initials: J
authors.lastname: Hammer
mesh_terms: D019295:Computational Biology / Q000379:methods*; D018604:Epitope Mapping / Q000379:methods*; D018984:Epitopes, T-Lymphocyte / Q000276:immunology*; D006680:HLA Antigens / Q000276:immunology*; D000949:Histocompatibility Antigens Class II / Q000276:immunology*; D006801:Humans; D010446:Peptide Fragments / Q000235:genetics / Q000276:immunology; D011237:Predictive Value of Tests
chemical_list: D018984:Epitopes, T-Lymphocyte; D006680:HLA Antigens; D000949:Histocompatibility Antigens Class II; D010446:Peptide Fragments