Neural network-based prediction of candidate T-cell epitopes
- Author(s)
- Honeyman, MC; Brusic, V; Stone, NL; Harrison, LC;
- Details
- Publication Year 1998-10,Volume 16,Issue #10,Page 966-969
- Journal Title
- NATURE BIOTECHNOLOGY
- Publication Type
- Journal Article
- Abstract
- Activation of T cells requires recognition by T-cell receptors of specific peptides bound to major histocompatibility complex (MHC) molecules on the surface of either antigen-presenting or target cells, These peptides, T-cell epitopes, have potential therapeutic applications, such as for use as vaccines. Their identification, however, usually requires that multiple overlapping synthetic peptides encompassing a protein antigen be assayed, which in humans, is limited by volume of donor blood. T-cell epitopes are a subset of peptides that bind to MHC molecules. We use an artificial neural network (ANN) model trained to predict peptides that bind to the MHC class II molecule HLA-DR4(*0401). Binding prediction facilitates identification of T-cell epitopes in tyrosine phosphatase IA-2, an autoantigen in DR4-associated type1 diabetes. Synthetic peptides encompassing IA-2 were tested experimentally for DR4 binding and T-cell proliferation in humans at risk for diabetes. ANN-based binding prediction was sensitive and specific, and reduced the number of peptides required for T-cell assay by more than half, with only a minor loss of epitopes. This strategy could expedite identification of candidate T-cell epitopes in diverse diseases.
- Publisher
- NATURE AMERICA INC
- Keywords
- PEPTIDE BINDING-SPECIFICITY; RHEUMATOID-ARTHRITIS; SELECTION; MOLECULES; COMMON
- Publisher's Version
- https://doi.org/10.1038/nbt1098-966
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 1998-10-01 12:00:00