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T细胞受体特异性景观透露通过新的体设计

Gian Marco Visani¹, Michael N Pun², Anastasia A Minervina³

¹Paul G. Allen School of Computer Science and Engineering, University of Washington, 85 E Stevens Way NE, Seattle, WA 98195, USA.

|September 15, 2025

概括

我们开发了一种计算方法来预测T细胞受体 (TCR) 与MHC上的的相互作用,并设计新的免疫性. 这种方法准确地预测TCR-pMHC结合和T细胞活性,有助于疫苗和工程T细胞治疗的发展.

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细胞对于适应性免疫至关重要,它们识别由MHC分子呈现的病原体.
预测T细胞受体 (TCR) - -MHC (pMHC) 相互作用至关重要,但由于反应性数据有限,具有挑战性.

研究的目的:

开发一种计算方法来预测TCR-pMHC相互作用,并设计新的免疫原性.
利用物理引导的机器学习模型 (HERMES) 来准确预测TCR识别和T细胞活动.
为设计免疫性,评估TCR特异性,并为T细胞疗法和疫苗提供信息提供一个平台.

主要方法:

利用 HERMES,一种基于结构的,以物理为导向的机器学习模型,在蛋白质宇宙中进行训练.
应用该模型来预测TCR-pMHC结合亲缘关系和T细胞活动的病毒表位和癌症新抗原.
开发了一个基于TCR识别模型的de novo设计协议,并通过实验验证.

主要成果:

与TCR-pMHC结合和T细胞活动预测的实验数据达到了高达0.72的相关性.
经过实验验证的de novo设计的,多达五次替代,在50%的病例中成功激活T细胞.
量化了各种TCR-MHC复合体的体识别景观多样性,为T细胞特异性提供了洞察力.

结论:

计算方法准确地预测了TCR-pMHC相互作用,并使免疫性的新设计成为可能.
这一框架为推进工程T细胞疗法,疫苗开发和了解T细胞特异性提供了巨大的潜力.

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