一种指纹方法,以开拓基于结构的T细胞受体谱系分析和特异性预测
Francesca Mayol-Rullan1,2, Marine Bugnon1,2, Marta A S Perez1,2
1Computer-aided Molecular Engineering Group, Department of Fundamental Oncology, Lausanne University, Ludwig Institute for Cancer Research, Epalinges, Switzerland.
Frontiers in immunology
|November 24, 2025
概括
一种新的方法,TCRfp,使用3D结构指纹快速比较T细胞受体 (TCRs) 并预测它们对-MHC复合物的特异性,改善癌症免疫疗法. 这种方法增强了免疫识别解码,以获得更好的治疗开发.
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:
- 癌症免疫治疗的发展正在加速.
- 了解T细胞受体 (TCR) 对-MHC (pMHC) 复合物的特异性,对于改善像采用细胞移植和疫苗接种这样的免疫疗法至关重要.
- 当前的计算方法在明确的TCR-pMHC配对方面扎,特别是在新型的pMHC中.
研究的目的:
- 开发一种新的基于物理,基于结构的计算方法,用于TCR-pMHC特异性预测.
- 克服现有的基于结构的方法的计算成本限制.
- 创建一个快速,高吞吐量的方法,用于TCR比较和曲目分析.
主要方法:
- 开发了TCRfp,这是一种使用电形5D (ES5D) 技术将3DTCR结构转化为1D结构指纹 (FP) 的方法.
- 模拟了超过15,000个配对的TCRα和β链的3D结构,具有已知的pMHC特异性.
- 将模拟的TCR编码为1D TCR指纹 (TCRfp).
主要成果:
- TCR FP之间的相似度与表位识别相关.
- 该TCR建模过程被转化为潜在的临床应用的快速管道.
- 在与基于序列的方法相结合时,TCRfp在预测私人数据集上的TCR特异性方面表现出卓越的表现.
结论:
- 基于3D结构的TCRfp提供了一种快速,高通量的TCR比较和曲目分析方法.
- 在预测TCR特异性方面,TCRfp超越了现有的方法,特别是当与基于序列的方法相结合时.
- TCRfp为基于序列的方法提供了结构信息的补充,可能会增强免疫识别解码,以改善癌症免疫疗法.
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