Understanding the mechanisms of T cell antigen recognition that underpin adaptive immune responses is critical for developing vaccines, immunotherapies and treatments against autoimmune diseases. Despite extensive research efforts, accurate prediction of T cell receptor (TCR)–antigen binding pairs remains a great challenge due to the vast diversity and cross-reactivity of TCRs. Here we propose a deep-learning-based framework termed epitope-anchored contrastive transfer learning (EPACT) tailored to paired human CD8⁺ TCRs. Harnessing the pretrained representations and co-embeddings of peptide–major histocompatibility complex (pMHC) and TCR, EPACT demonstrated generalizability in predicting binding specificity for unseen epitopes and distinct TCR repertoires. Contrastive learning enabled highly precise predictions for immunodominant epitopes and interpretable analysis of epitope-specific T cells. We applied EPACT to SARS-CoV-2-responsive T cells, and the predicted binding strength aligned well with the surge in spike-specific immune responses after vaccination. We further fine-tuned EPACT on structural data to decipher the residue-level interactions involved in TCR–antigen recognition. EPACT was capable of quantifying interchain distance matrices and identifying contact residues, corroborating the presence of TCR cross-reactivity across multiple tumour-associated antigens. Together, EPACT can serve as a useful artificial intelligence approach with important potential in practical applications and contribute towards the development of TCR-based immunotherapies.

了解支撑适应性免疫反应的 T 细胞抗原识别机制对于开发针对自身免疫性疾病的疫苗、免疫疗法和治疗方法至关重要。尽管进行了广泛的研究工作，但由于 TCR 的巨大多样性和交叉反应性，准确预测 T 细胞受体 （TCR）-抗原结合对仍然是一项巨大的挑战。在这里，我们提出了一个基于深度学习的框架，称为表位锚定对比迁移学习 （EPACT），专为成对的人类 CD8⁺ TCR 量身定制。利用肽-主要组织相容性复合物 （pMHC） 和 TCR 的预训练表示和共嵌入，EPACT 在预测不可见表位和不同 TCR 库的结合特异性方面表现出普遍性。对比学习能够对免疫显性表位进行高精度预测，并对表位特异性 T 细胞进行可解释分析。我们将 EPACT 应用于 SARS-CoV-2 反应性 T 细胞，预测的结合强度与接种疫苗后刺突特异性免疫反应的激增非常吻合。我们进一步对结构数据微调了 EPACT，以破译 TCR-抗原识别中涉及的残基水平相互作用。EPACT 能够量化链间距离矩阵并识别接触残基，证实多种肿瘤相关抗原之间存在 TCR 交叉反应性。总之，EPACT 可以作为一种有用的人工智能方法，在实际应用中具有重要潜力，并为基于 TCR 的免疫疗法的开发做出贡献。