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用AI/ML赋权的方法来预测T细胞介导免疫力及其他疾病

Cheng-Chi Chao^1,2, Yulun Chiu^3,4, Lucas Yeung¹

¹Terasaki Institute for Biomedical Innovation, Los Angeles, CA, United States.

Frontiers in immunology

|September 15, 2025

概括

像AlphaFold 3这样的AI模型现在可以预测T细胞受体与-MHC复合物的相互作用. 这一突破有助于识别与疾病相关的表位,以改善免疫疗法和疫苗开发.

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:

在免疫和自身免疫方面,T细胞具有双重作用,需要精确调节T细胞受体 (TCR) - /主要基因相容性复合体 (pMHC) 相互作用.
准确预测TCR-pMHC特异性对于开发癌症,感染和自身免疫性疾病的有效治疗方法至关重要.
目前TCR-pMHC特异性的预测模型仍处于早期开发阶段.

研究的目的:

评估AlphaFold 3 (AF3) 在预测TCR表位特异性的潜力.
探索基于深度学习的结构建模在理解TCR-pMHC相互作用中的应用.

主要方法:

利用AlphaFold 3进行人工智能驱动的TCR-pMHC相互作用计算.
评估AlphaFold 3在区分有效和无效表位图的准确性.
用于在高通量过程中用于表位标识.

主要成果:

阿尔法3证明了模拟TCR-pMHC相互作用的能力.
该模型在区分免疫原性表位上显示出越来越高的准确性.
成功确定了疫苗开发和治疗T细胞设计的潜在表位.

结论:

基于深度学习的结构建模,以AlphaFold 3为例,显示了可普遍化的TCR-pMHC相互作用预测的前景.
准确的TCR-pMHC预测模型可以显著推进T细胞介导免疫疗法和药物设计.
精确预测T细胞免疫性,对各种疾病具有巨大的治疗潜力.

关键词:

由AI/ML驱动的结构预测.设计T细胞治疗的设计在TCR-pMHC识别过程中,免疫性建模的免疫性建模.蛋白质与蛋白质的相互作用

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