DapPep可精准预测未知抗原的T细胞受体结合亲和力，助力免疫治疗研发。
DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction
- 采用自适应无肽学习框架，融合预训练蛋白语言模型与自监督机制。
- 在数据稀疏和未见抗原场景下表现优于现有工具，提升泛化能力。
- 适用于肿瘤新抗原治疗中反应性T细胞筛选等临床任务。
识别能与抗原肽结合的T细胞受体（TCRs）是开发疫苗和免疫疗法的技术基础。现有深度学习方法虽能从已知TCRs中学习抗原结合模式，但在面对新发或稀疏表示的抗原时表现不佳。而对未见抗原或外源肽的结合特异性至关重要。本文提出一种领域自适应的无肽学习框架DapPep，用于通用的TCR-抗原结合亲和力预测。该框架采用轻量级自注意力结构，结合预训练蛋白语言模型与内循环自监督机制，生成鲁棒的TCR-肽表示。在多个基准数据集上的实验表明，DapPep持续优于现有工具，尤其在数据稀缺及未见肽场景下展现出强大泛化能力。此外，DapPep在肿瘤新抗原治疗中的反应性T细胞筛选及三维结构关键位点识别等挑战性临床任务中亦表现有效。
原文摘要 · Abstract (English)
Identifying T-cell receptors (TCRs) that interact with antigenic peptides provides the technical basis for developing vaccines and immunotherapies. The emergent deep learning methods excel at learning antigen binding patterns from known TCRs but struggle with novel or sparsely represented antigens. However, binding specificity for unseen antigens or exogenous peptides is critical. We introduce a domain-adaptive peptide-agnostic learning framework DapPep for universal TCR-antigen binding affinity prediction to address this challenge. The lightweight self-attention architecture combines a pre-trained protein language model with an inner-loop self-supervised regime to enable robust TCR-peptide representations. Extensive experiments on various benchmarks demonstrate that DapPep consistently outperforms existing tools, showcasing robust generalization capability, especially for data-scarce settings and unseen peptides. Moreover, DapPep proves effective in challenging clinical tasks such as sorting reactive T cells in tumor neoantigen therapy and identifying key positions in 3D structures.
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