Neural network-based prediction of candidate T-cell epitopes.
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.
Honeyman
M C
MC
The Walter and Eliza Hall Institute of Medical Research, Royal Melbourne Hospital, Victoria, Australia.
Brusic
V
V
Stone
N L
NL
Harrison
L C
LC
D000595:Amino Acid Sequence; D000939:Epitopes / Q000737:chemistry*; D015395:Histocompatibility Antigens Class I / Q000276:immunology; D006720:Homozygote; D006801:Humans; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D017386:Sequence Homology, Amino Acid; D013601:T-Lymphocytes / Q000276:immunology*
D000939:Epitopes; D015395:Histocompatibility Antigens Class I