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一个生物知情的视觉导向框架,用于可解释的T细胞受体-位结合预测

Yajing Yuan¹, Junwei Chen¹, Yufang Zhang²

¹State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200040, P. R. China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|November 7, 2025

概括

一个新的深度学习框架,DAISY,准确地预测T细胞受体 (TCR) 和癌症免疫治疗的表皮质结合. 它通过整合物理化学性质,超越现有模型,帮助预测患者的存活率,并推进免疫建模.

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:

准确预测T细胞受体 (TCR) 结合表皮质对癌症免疫治疗至关重要.
目前的预测模型面临的挑战是概括和结合关键的物理化学性质.

研究的目的:

提出DAISY,一个生物信息,视觉引导的深度学习框架,用于可靠和可解释的TCR-epitope绑定预测.
为了改进对未见的表观特征的概括,并整合物理化学性质.

主要方法:

DAISY集成了使用条件适应融合模块的等级物理化学特征.
它模拟了残留水平的空间相互作用和全球生化背景.
评分-CAM可视化通过定位与交互相关的残留物提供可解释性.

主要成果:

在四种概括情景中,DAISY 始终优于最先进的模型.
在未见对设置中,ROC-AUC得到了11%的改善,PR-AUC得到了16%的改善.
预测与T细胞克隆扩张,功能性TCR识别和患者生存预测相关.

结论:

戴西提供了一个强大的工具,用于翻译免疫学和免疫建模.
它为下一代免疫建模提供了一个可扩展的范式.
该框架增强了用于免疫治疗应用的TCR-表皮质相互作用的预测.

关键词:

TCR表皮质相互作用相互作用.基于生物学的建模建模.癌症免疫疗法免疫疗法物理化学特性物理化学特性视觉指导学习是指导学习.

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