Barton Justin, Gore Trupti, Phanichkrivalkosil Meghna, Shepherd Adrian, Mishto Michele
School of Natural Sciences and Institute of Structural and Molecular Biology, Birkbeck, University of London, London, UK.
Centre for Inflammation Biology and Cancer Immunology & Peter Gorer Department of Immunobiology, King's College London, London, UK.
Eur J Immunol. 2025 Jul;55(7):e51607. doi: 10.1002/eji.202451607.
The ability to predict which antigenic peptide(s) the αβTCR of a given CD8 T-cell clone can recognise would represent a quantum leap in the understanding of T-cell repertoire selection and development of targeted cell-mediated immunotherapies. Current methods fail to make accurate predictions for antigenic peptides not present in the training dataset. Here, we propose a novel deep learning method called nuTCRacker that makes accurate predictions for a subset of unseen peptides, with an AUC > 0.7 for around a third of peptides evaluated using a large dataset compiled from curated public resources. An additional evaluation was undertaken using a small cellula-validated dataset of αβTCR peptides associated with cancer. Our analysis suggests that it is possible to make useful predictions for an unseen peptide provided the training dataset contains: many samples with the same HLA class I molecule as that bound to the peptide; at least one peptide that is similar to the target peptide; and a small number of αβTCRs that are similar to those bound to the unseen peptide of interest.
预测给定CD8 T细胞克隆的αβTCR能够识别哪些抗原肽，这将在理解T细胞库选择和靶向细胞介导免疫疗法的发展方面实现巨大飞跃。目前的方法无法对训练数据集中不存在的抗原肽做出准确预测。在此，我们提出了一种名为nuTCRacker的新型深度学习方法，该方法能够对一部分未见的肽段做出准确预测，对于使用从精心整理的公共资源汇编而成的大型数据集评估的约三分之一的肽段，其曲线下面积（AUC）> 0.7。我们还使用了一个经过细胞验证的与癌症相关的αβTCR肽段的小数据集进行了额外评估。我们的分析表明，如果训练数据集包含以下内容，就有可能对未见的肽段做出有用的预测：许多与该肽段结合的HLA I类分子相同的样本；至少一个与目标肽段相似的肽段；以及少量与感兴趣的未见肽段结合的αβTCR相似的αβTCR。
