Ye Fei, Chen Mao, Huang Yixuan, Zhang Ruihao, Li Xuqi, Wang Xiuyuan, Han Sanyang, Ma Lan, Liu Xiao
Institute of Biopharmaceutical and Health Engineering, Tsinghua ShenZhen International Graduate School, Tsinghua University, Lishui Road, Nanshan District, Shenzhen, Guangdong Province 518055, China.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf246.
Identification of T cell receptor (TCR) specificities for antigens from large-scale single-cell or bulk TCR repertoire data plays a vital role in disease diagnosis and immunotherapy. In silico prediction models have emerged in recent years. However, the generalizability and transferability of current computational models remain significant hurdles in accurately predicting TCR-pMHC binding specificity, primarily due to the limited availability of experimental data and the vast diversity of TCR sequences. In this paper, we propose a lightweight contrastive TCR-pMHC learning with context-aware prompts, named LightCTL, to infer TCR-pMHC binding specificity. For each TCR and peptide-MHC sequence, we utilize a TCR encoding module and a pMHC encoding module to transform them into latent representations. Specifically, we introduce a contrastive TCR-pMHC learning paradigm to enhance the generalization ability of TCR-pMHC binding specificity prediction by learning the matching relationship between TCR-pMHC and MHC-peptide. We fuse the TCR and pMHC latent representations and employ a novel context-aware prompt module to consider the varying importance of different feature maps. Compared with existing methods, LightCTL substantially improves the accuracy of predicting TCR-pMHC binding specificity. Moreover, comparative experiments across eight independent datasets demonstrate the generalization ability of LightCTL, showing superior performance for predicting unknown TCR-pMHC pairs. Finally, we assess LightCTL's efficacy across different TCR sequence lengths and distinct unseen epitopes, as well as estimate cytomegalovirus-specific TCR diversity and clone frequency from peripheral TCR repertoire data. Overall, our findings highlight LightCTL as a versatile analytical method for advancing novel T-cell therapies and identifying novel biomarkers for disease diagnosis.
从大规模单细胞或整体T细胞受体（TCR）库数据中识别针对抗原的TCR特异性在疾病诊断和免疫治疗中起着至关重要的作用。近年来出现了计算机预测模型。然而，当前计算模型的通用性和可转移性仍然是准确预测TCR-pMHC结合特异性的重大障碍，主要原因是实验数据有限以及TCR序列的巨大多样性。在本文中，我们提出了一种具有上下文感知提示的轻量级对比TCR-pMHC学习方法，名为LightCTL，以推断TCR-pMHC结合特异性。对于每个TCR和肽-MHC序列，我们利用一个TCR编码模块和一个pMHC编码模块将它们转换为潜在表示。具体来说，我们引入了一种对比TCR-pMHC学习范式，通过学习TCR-pMHC与MHC-肽之间的匹配关系来提高TCR-pMHC结合特异性预测的泛化能力。我们融合TCR和pMHC潜在表示，并采用一个新颖的上下文感知提示模块来考虑不同特征图的不同重要性。与现有方法相比，LightCTL显著提高了预测TCR-pMHC结合特异性的准确性。此外，在八个独立数据集上的对比实验证明了LightCTL的泛化能力，显示出在预测未知TCR-pMHC对方面的卓越性能。最后，我们评估了LightCTL在不同TCR序列长度和不同未见表位上的功效，以及从外周TCR库数据估计巨细胞病毒特异性TCR多样性和克隆频率。总体而言，我们的研究结果突出了LightCTL作为一种通用分析方法，可推动新型T细胞疗法的发展并识别用于疾病诊断的新型生物标志物。
