SageTCR:一个基于结构的模型,集成了残留和原子层次表示,用于增强的TCR-pMHC结合预测.
Xiangyi Li1, Chuance Sun1, Weiran Huang1
1Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Dongchuan Road, Minhang District, Shanghai 200240, China.
Briefings in bioinformatics
|September 23, 2025
概括
新型图形神经网络SageTCR使用结构数据准确预测T细胞受体 (TCR) 和-MHC (pMHC) 相互作用. 该框架通过改善对免疫反应的理解来增强基于TCR的疗法.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- T细胞受体 (TCRs) 对于适应性免疫至关重要,它们能识别-MHC (pMHC) 复合体.
- 由于TCRs的固有多样性和交叉反应性,对预测TCR-表皮质相互作用和开发基于TCR的疗法提出了重大挑战.
研究的目的:
- 引入SageTCR,一个双层图形神经网络 (GNN) 框架,旨在预测TCR-pMHC结合可能性.
- 利用结构数据和预训练的语言模型来提高预测准确度.
主要方法:
- SageTCR采用GNN架构来编码残留和原子级别的结构信息.
- 注意力机制整合了双模表示,数据增强策略解决了实验结构的稀缺性.
- 该框架保留了TCR-pMHC相互作用的特征对角结合模式.
主要成果:
- 在预测TCR-pMHC结合方面,SageTCR显著优于其他六种深度学习方法.
- 该模型通过识别关键的接触残留物及其在接口上的形状特征来证明可解释性.
结论:
- SageTCR提供了一种强大而准确的方法来预测TCR-pMHC相互作用,推进TCR相关的疗法.
- 该框架的可解释性为TCR工程和免疫疗法的设计提供了宝贵的见解.
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