Strategies for identifying and predicting islet autoantigen T-cell epitopes in insulin-dependent diabetes mellitus
- Author(s)
- Honeyman, MC; Brusic, V; Harrison, LC;
- Details
- Publication Year 1997-10,Volume 29,Issue #5,Page 401-404
- Journal Title
- ANNALS OF MEDICINE
- Publication Type
- Journal Article
- Abstract
- T cells recognize peptide epitopes bound to major histocompatibility complex molecules. Human T-cell epitopes have diagnostic and therapeutic applications in autoimmune diseases. However, their accurate definition within an autoantigen by T-cell bioassay, usually proliferation, involves many costly peptides and a large amount of blood, We have therefore developed a strategy to predict T-cell epitopes and applied it to tyrosine phosphatase IA-2, an autoantigen in IDDM, and HLA-DR4(*0401). First, the binding of synthetic overlapping peptides encompassing IA-2 was measured directly to purified DR4. Secondly, a large amount of HLA-DR4 binding data were analysed by alignment using a genetic algorithm and were used to train an artificial neural network to predict the affinity of binding. This bioinformatic prediction method was then validated experimentally and used to predict DR4 binding peptides in IA-2. The binding set encompassed 85% of experimentally determined T-cell epitopes. Both the experimental and bioinformatic methods had high negative predictive values, 92% and 95%, indicating that this strategy of combining experimental results with computer modelling should lead to a significant reduction in the amount of blood and the number of peptides required to define T-cell epitopes in humans.
- Publisher
- BLACKWELL SCIENCE LTD
- Keywords
- MAJOR HISTOCOMPATIBILITY COMPLEX; PEPTIDE
- Publisher's Version
- https://doi.org/10.3109/07853899708999369
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 1997-10-01 12:00:00