结合k的T细胞受体具有定义的序列特征
Hyunjin Park1, Jonathan Krog2,3, Brandon Carter1
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States.
Frontiers in immunology
|December 19, 2025
概括
了解T细胞受体 (TCR) 交叉反应性对于适应性免疫至关重要. 这项研究揭示了TCR序列如何编码交叉反应,这对治疗TCR发育和免疫监测有影响.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- T细胞受体 (TCRs) 识别了免疫监测的多种-MHC标.
- 对于TCR交叉活性的序列基础仍然不太了解.
研究的目的:
- 在数以百万计的TCR序列和七种相关中表征TCR k-结合.
- 确定TCR序列,特别是CDR3区域在编码交叉反应性的作用.
主要方法:
- 利用体外测试来测试约4700万个TCRs对七个MHC标的k-结合.
- 开发了一种使用BLOSUM-50嵌入的机器学习模型来预测TCR--MHC结合.
- 通过在机器学习模型中掩盖单个残留物来评估CDR3残留物的重要性.
主要成果:
- 确定了影响k-结合的TCR CDR3残留重要性等级.
- 使用BLOSUM-50嵌入式模型实现了TCR-pMHC结合的高预测性能 (F1=0.698,AUPRC=0.745).
- 发现残留物重要性排名与计算阿拉宁扫描结果相关.
结论:
- TCR序列,特别是CDR3,决定了交叉反应性.
- 机器学习模型可以有效地预测TCR-pMHC结合和CDR3残留的重要性.
- 由于固有的交叉反应性,治疗TCR需要仔细评估特异性.
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