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TCRdesign:一种抗原特异性生成语言模型,用于T细胞受体的新设计

Xiaokun Li^1,2,3,4, Qiang Yang^1,5, Long Xu¹

¹Faculty of Computing, Harbin Institute of Technology, West Dazhi Street, 150001 Harbin, China.

Briefings in bioinformatics

|December 26, 2025

概括

我们开发了TCRdesign,这是一种用于设计具有特定抗原结合能力的T细胞受体 (TCR) 的AI工具. 这使得人工智能驱动的T细胞工程通过创建新的TCR序列来推进个性化免疫疗法.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
人工智能的人工智能

背景情况:

T细胞受体 (TCR) 对于免疫疗法至关重要,通过α和β链识别外来抗原.
目前的TCR工程方法是劳动密集型的,依赖于分离自然TCR和广泛的湿实验室验证.
使用人工智能的治疗TCRs的de novo设计仍然是一个未经探索的领域.

研究的目的:

开发一个人工智能驱动的平台,用于对抗原特异性T细胞受体 (TCR) 的新设计.
创建可预测的抗原结合特异性的人工TCRβ链互补性确定区域3序列.
提高用于免疫治疗应用的TCR工程的效率和准确性.

主要方法:

开发了TCR设计,一种生成蛋白语言模型 (PLM) 用于新的TCR序列设计.
将抗原结合特异性调节集成到TCR设计过程中.
创建了TCRBinder,一种用于评估TCR-抗原结合特异性的预测模型,使用配对链和抗原序列信息.

主要成果:

TCRdesign生成了抗原特定的TCR序列,其性能优于最先进的方法.
与天然的TCR相比,人工智能设计的TCR序列显示出对瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤瘤.
该模型确定了参与TCR抗原结合的关键残留物,提高了可解释性.

结论:

TCRdesign有效地生成具有所需抗原特异相互作用的TCR序列.
人工智能平台为T细胞工程在精密免疫治疗中提供了一种多功能方法.
这项工作为通过人工智能驱动的设计加速开发新型免疫疗法铺平了道路.

关键词:

TCR 序列设计的设计产生抗原特异性的基因.编码器解码器框架模型的解释性可解释性预训练技术预训练技术蛋白质大型语言模型

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