Neural network-based prediction of candidate T-cell epitopes

Margo C. Honeyman, Vladimir Brusic, Natalie L. Stone, Leonard C. Harrison

Research output: Journal PublicationArticlepeer-review

162 Citations (Scopus)


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 type 1 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.

Original languageEnglish
Pages (from-to)966-969
Number of pages4
JournalNature Biotechnology
Issue number10
Publication statusPublished - 1998
Externally publishedYes


  • Applied immunology
  • Bioinformatics
  • MHC

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Molecular Medicine
  • Biomedical Engineering


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