STRATEGIES FOR IDENTIFYING AND PREDICTING ISLET AUTOANTIGEN T-CELL EPITOPES IN INSULIN-DEPENDENT DIABETES-MELLITUS

T cells recognize peptide epitopes bound to major histocompatibility c omplex molecules. Human T-cell epitopes have diagnostic and therapeuti c applications in autoimmune diseases. However, their accurate definit ion within an autoantigen by T-cell bioassay, usually proliferation, i nvolves many costly peptides and a large amount of blood, We have ther efore developed a strategy to predict T-cell epitopes and applied it t o tyrosine phosphatase IA-2, an autoantigen in IDDM, and HLA-DR4(0401 ). First, the binding of synthetic overlapping peptides encompassing I A-2 was measured directly to purified DR4. Secondly, a large amount of HLA-DR4 binding data were analysed by alignment using a genetic algor ithm and were used to train an artificial neural network to predict th e affinity of binding. This bioinformatic prediction method was then v alidated experimentally and used to predict DR4 binding peptides in IA -2. The binding set encompassed 85% of experimentally determined T-cel l epitopes. Both the experimental and bioinformatic methods had high n egative 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 pe ptides required to define T-cell epitopes in humans.