Honeyman M C, Brusic V, Stone N L, Harrison L C
The Walter and Eliza Hall Institute of Medical Research, Royal Melbourne Hospital, Victoria, Australia.
Nat Biotechnol. 1998 Oct;16(10):966-9. doi: 10.1038/nbt1098-966.
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.
T细胞的激活需要T细胞受体识别与抗原呈递细胞或靶细胞表面主要组织相容性复合体（MHC）分子结合的特定肽段。这些肽段，即T细胞表位，具有潜在的治疗应用，比如用作疫苗。然而，其鉴定通常需要对包含蛋白质抗原的多个重叠合成肽段进行检测，而在人类中，这受到供体血量的限制。T细胞表位是与MHC分子结合的肽段子集。我们使用经过训练的人工神经网络（ANN）模型来预测与II类MHC分子HLA-DR4（*0401）结合的肽段。结合预测有助于鉴定酪氨酸磷酸酶IA-2中的T细胞表位，IA-2是DR4相关1型糖尿病中的一种自身抗原。对包含IA-2的合成肽段进行了实验测试，以检测其在有糖尿病风险的人类中的DR4结合情况和T细胞增殖情况。基于ANN的结合预测具有敏感性和特异性，将T细胞检测所需的肽段数量减少了一半以上，仅少量表位丢失。该策略可加快多种疾病中候选T细胞表位的鉴定。
