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ProTCR:一种蛋白质语言模型驱动的框架,用于解码TCR-抗原识别,用于精确免疫疗法

Minrui Xu^1,2, Manman Lu^1,3, Peng Liu¹

¹Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China.

Briefings in bioinformatics

|January 11, 2026

概括

一个新的计算模型,ProTCR,准确地预测T细胞受体 (TCR) 与的相互作用. 这通过确定癌症和SARS-CoV-2等传染病的治疗点来推进基于TCR的免疫疗法.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

T细胞受体 (TCRs) 对于适应性免疫至关重要,它们识别新抗原以启动免疫反应.
确定特定的TCR-相互作用对于开发有效的基于TCR的免疫疗法至关重要,但仍然是一个重大挑战.
现有的方法通常依赖于已知的-TCR对,限制了它们的适用性.

研究的目的:

开发一种新的计算模型,ProTCR,用于准确预测TCR-识别.
提高TCR识别模式分析的通用性和生物解释性.
为设计精密免疫疗法提供计算基础.

主要方法:

将蛋白质语言模型ProtT5与双通道网络中的深度学习技术集成.
利用全球和本地特征提取来实现高效的氨基酸序列表示.
跨多种数据集的验证,包括新抗原,新和MHCII类受限制的表位.

主要成果:

ProTCR在各种数据集中展示了强大的性能和广泛的适用性,超过了以前的方法.
该模型准确地预测了未见的和各种抗原的TCR-相互作用.
当应用于临床相关场景时,观察到一致的高精度和稳定性,包括癌症免疫疗法和病原体识别 (流感,SARS-CoV-2).

结论:

ProTCR提供了一种强大而通用的工具,用于阐明免疫反应机制.
该模型克服了以前方法的局限性,因为它不仅仅依赖已知的TCR-对.
ProTCR为推进基于新抗原和TCR的精密免疫疗法策略提供了强大的计算基础.

关键词:

这是一种TCR-.模型的解释性可解释性精确免疫疗法精确免疫疗法蛋白质语言模型

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