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通过免疫指纹的几何深度学习来解码TCR识别

Chun Shang^1,2, Kevin C Chan³, Ruhong Zhou^1,2,4,5

¹College of Physics, College of Life Sciences, and Institute of Quantitative Biology, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.

Briefings in bioinformatics

|March 16, 2026

概括

一个新的深度学习模型通过预测-MHC结合来解码T细胞受体 (TCR) 识别. 这个框架揭示了"免疫学指纹",以了解免疫反应和指导治疗.

科学领域:

免疫学免疫学免疫学
结构生物学结构生物学
计算生物学计算生物学

背景情况:

对于适应性免疫,病原体防御和自我耐受性来说,T细胞受体 (TCR) 对主要基因相容性复合体 (pMHC) 的识别至关重要.
尽管有大量的结构数据,但了解TCR特异性和交叉反应性仍然具有挑战性.
现有的结构分析很难捕捉控制TCR-pMHC相互作用的复杂特征.

研究的目的:

开发一个深度学习框架,用于预测pMHC分子的TCR识别.
确定驱动TCR-pMHC参与的关键结构和物理化学特征.
为TCR特异性和交叉反应性提供可解释的见解.

主要方法:

开发了一个多式几何深度学习框架,从pMHC接口中提取空间和物理化学特征.
将模型应用于HLA-A*02--TCR晶体结构的数据集,用于培训和验证.
集成了一个可解释性模块,以识别关键相互作用残留物和动机.

主要成果:

深度学习模型准确地预测了TCR的约束偏好.
确定了独特的接口"免疫学指纹",这决定了TCR识别.
透露了使用HLA-B*27复合体的自我和细菌之间的潜在TCR交叉反应.

结论:

建立了一个可扩展的,基于结构的深度学习方法来解码T细胞识别.
该框架为TCR特异性决定因素提供了可解释的见解.
为抗原设计,疫苗开发和基于TCR的免疫疗法提供了强大的工具.

关键词:

TCRpMHC的识别方式抗原的设计设计.几何深度学习的几何深度学习免疫学指纹免疫学指纹结构性免疫信息学

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