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G2VTCR:通过T细胞受体序列的Weisfeiler-Lehman图嵌入来预测抗原结合特异性

Zicheng Wang¹, Yufeng Shen^1,2,3

¹Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.

bioRxiv : the preprint server for biology

|July 14, 2025

概括

一种新的计算方法,G2VTCR,使用原子图嵌入来预测T细胞受体 (TCR) 和抗原识别. 这种方法通过改进TCR抗原结合预测和聚类来增强对适应性免疫的理解.

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:

结合-MHC复合体的T细胞受体 (TCR) 对适应性免疫至关重要.
高通量测试提供了TCR抗原特异性的数据,但由于TCR的多样性,鉴定特定的TCR-抗原对是具有挑战性的.

研究的目的:

引入G2VTCR,一种新的计算方法,用于使用图形嵌入来预测TCR-抗原识别.
为了证明G2VTCR在TCR抗原结合预测和TCR聚类中的有效性.

主要方法:

G2VTCR表示抗原和TCR CDR3序列作为原子级图.
图形嵌入式是使用Weisfeiler-Lehman代生成的.
监督分类和无监督集群任务用于评估.

主要成果:

在分类和聚类方面,G2VTCR的性能优于现有的嵌入方法,包括预训练的蛋白质语言模型.
该研究调查了Weisfeiler-Lehman代和TCR CDR3样本大小对性能的影响.

结论:

免疫谱系序列的原子级图形嵌入是预测抗原特异性的强大工具.
在分析TCR抗原相互作用方面,G2VTCR提供了显著的进步.

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