Neural network-based prediction of candidate T-cell epitopes
Neural network-based prediction of candidate T-cell epitopes
复制标题
DOI：
10.1038/nbt1098-966
复制
发表时间：
1998-10-01
影响因子：
46.9
通讯作者：
Harrison, LC
中科院分区：
文献类型：
--
作者：
Honeyman, MC;Brusic, V;Harrison, LC
T细胞的活化需要T细胞受体识别与抗原呈递细胞或靶细胞表面上的主要组织相容性复合体（MHC）分子结合的特异性肽。这些肽（T细胞表位）具有潜在的治疗应用，例如用作疫苗。然而，它们的鉴定通常需要测定包含蛋白抗原的多个重叠合成肽，这在人类中受到供体血液体积的限制。T细胞表位是与MHC分子结合的肽的子集。我们使用人工神经网络（ANN）模型训练，以预测与MHC II类分子HLA-DR 4（*0401）结合的肽。结合预测有助于识别酪氨酸磷酸酶IA-2（DR 4相关1型糖尿病的自身抗原）中的T细胞表位。实验测试了包含IA-2的合成肽在有糖尿病风险的人中的DR 4结合和T细胞增殖。基于人工神经网络的结合预测是敏感和特异的，并减少了一半以上的T细胞测定所需的肽的数量，只有一个微小的表位损失。这种策略可以加快在不同疾病中识别候选T细胞表位。
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.