T细胞通过诱发对癌细胞和病原体的反应，同时对自身抗原保持耐受，对适应性免疫系统发挥至关重要的作用，这引发了对开发各种以T细胞为重点的免疫疗法的兴趣。然而，鉴定被T细胞识别的抗原是低通量和繁琐的。为了克服这些限制的一些，出现了用于预测CD8+ T细胞表位的计算方法。尽管近年来有所发展，但大多数免疫原性算法难以从小型数据集中学习肽免疫原性特征，受到HLA偏倚的影响并且无法可靠地预测特定于病理学的CD8+ T细胞表位。我们开发了名为TRAP（HLA-I呈递的肽段对T细胞识别潜力）的强大深度学习工作流，用于预测MHC-I呈递的致病和自身肽段的CD8+ T细胞表位。TRAP使用迁移学习、深度学习架构和MHC结合信息来对CD8+ T细胞表位进行上下文特异性预测。TRAP还检测那些与训练数据集中的肽段显著不同的低置信度预测，以免产生错误预测。为了估计那些具有低置信度预测的致病肽段的免疫原性，我们进一步开发了一种新的度量指标RSAT（相对类似自身抗原和肿瘤相关抗原）作为癌症研究中“与自身不同”的补充。TRAP被用于鉴定神经胶质母细胞瘤患者以及SARS-CoV-2肽段中的表位，并在癌症和致病设置中表现出优于其他算法的性能。TRAP在从新出现的病原体的受限数据中提取与免疫原性相关的特性方面尤为有效，并将其转化为相关物种，并且能够最小化不平衡数据集中可能的表位损失。我们还证明了这种称为RSAT的新指标能够估计不同长度和物种的致病肽段的免疫原性。TRAP的实施代码可在以下网址获得：https://github.com/ChloeHJ/TRAP。本研究提出了一种新的计算工作流，以准确预测CD8+ T细胞表位，促进对抗原特异性T细胞应答的更好理解和有效临床治疗的开发。©2023年。BioMed Central Ltd.，为Springer Nature的一部分。

T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes.We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies.TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP .This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.© 2023. BioMed Central Ltd., part of Springer Nature.