arXiv 2503.006482025-09-04q-bio.QMcs.LG

通过从头肽设计揭示T细胞受体特异性景观

T-cell receptor specificity landscape revealed through de novo peptide design

Paul G. Allen School of Computer Science and Engineering, University of Washington（保罗·G·阿伦计算机科学与工程学院，华盛顿大学）；Department of Physics, University of Washington（物理系，华盛顿大学）；St. Jude Children’s Research Hospital（圣 Jude儿童研究医院）；Fred Hutchinson Cancer Center（弗雷德 Hutchinson癌症中心）；Department of Applied Mathematics, University of Washington（应用数学系，华盛顿大学）

Gian Marco Visani, Michael N. Pun, Anastasia A. Minervina, Philip Bradley, Paul Thomas, Armita Nourmohammad

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AI总结：

该研究提出基于HERMES模型的计算方法，可预测TCR-pMHC相互作用并从头设计免疫原性肽，经实验验证成功率达50%，还能量化肽识别景观多样性，为TCR特异性研究及免疫疗法、疫苗设计提供平台。

摘要：

T细胞通过对多种病原体产生特异性应答，在适应性免疫中发挥关键作用。T细胞受体（TCRs）与主要组织相容性复合体（MHCs）呈递的病原体衍生肽之间的有效结合介导免疫应答。然而，由于T细胞反应性的功能数据有限，预测这些相互作用仍然具有挑战性。在这里，我们提出了一种计算方法，用于预测TCR与MHC I类等位基因呈递的肽的相互作用，并为特定的TCR-MHC复合物设计新的免疫原性肽。我们的方法利用HERMES——一种在蛋白质宇宙上训练的、基于结构的、物理引导的机器学习模型——根据局部结构环境预测氨基酸偏好。尽管没有直接在TCR-pMHC数据上训练，HERMES中的隐式物理推理使我们能够准确预测不同病毒表位和癌症新抗原的TCR-pMHC结合亲和力和T细胞活性，与实验数据的相关性最高可达0.72。利用我们的TCR识别模型，我们开发了一种从头设计免疫原性肽的计算方案。通过在三个靶向病毒和癌症肽的TCR-MHC系统中进行实验验证，我们证明我们的设计（与天然序列相比最多有5个取代）激活T细胞的成功率最高可达50%。最后，我们使用我们的生成框架来量化各种TCR-MHC复合物的肽识别景观的多样性，为人类和小鼠的T细胞特异性提供关键见解。我们的方法为免疫原性肽和新抗原设计以及评估TCR特异性提供了一个平台，为工程化T细胞疗法和疫苗的设计提供了一个计算框架。

英文摘要：

T-cells play a key role in adaptive immunity by mounting specific responses against diverse pathogens. An effective binding between T-cell receptors (TCRs) and pathogen-derived peptides presented on Major Histocompatibility Complexes (MHCs) mediate an immune response. However, predicting these interactions remains challenging due to limited functional data on T-cell reactivities. Here, we introduce a computational approach to predict TCR interactions with peptides presented on MHC class I alleles, and to design novel immunogenic peptides for specified TCR-MHC complexes. Our method leverages HERMES, a structure-based, physics-guided machine learning model trained on the protein universe to predict amino acid preferences based on local structural environments. Despite no direct training on TCR-pMHC data, the implicit physical reasoning in HERMES enables us to make accurate predictions of both TCR-pMHC binding affinities and T-cell activities across diverse viral epitopes and cancer neoantigens, achieving up to 0.72 correlation with experimental data. Leveraging our TCR recognition model, we develop a computational protocol for de novo design of immunogenic peptides. Through experimental validation in three TCR-MHC systems targeting viral and cancer peptides, we demonstrate that our designs -- with up to five substitutions from the native sequence -- activate T-cells at success rates of up to 50%. Lastly, we use our generative framework to quantify the diversity of the peptide recognition landscape for various TCR-MHC complexes, offering key insights into T-cell specificity in both humans and mice. Our approach provides a platform for immunogenic peptide and neoantigen design, as well as for evaluating TCR specificity, offering a computational framework to inform design of engineered T-cell therapies and vaccines.

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