人类T细胞受体识别NRAS癌症新抗原的结构性表征和AlphaFold建模
Daichao Wu1,2, Rui Yin2,3, Guodong Chen1
1Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital, Laboratory of Structural Immunology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Science advances
|November 22, 2024
概括
了解T细胞受体 (TCR) 对癌症新抗原的识别是免疫治疗的关键. 这项研究揭示了NRAS新抗原的TCR双重识别的结构基础,并评估了用于预测这些相互作用的AI模型.
科学领域:
- 免疫学 免疫学 免疫学
- 结构生物学 结构生物学
- 计算生物学 计算生物学
背景情况:
- T细胞受体 (TCR) 对于识别癌症新抗原,驱动抗癌免疫反应和免疫疗法的有效性至关重要.
- 阐明TCR-新抗原识别的结构基础对于理解特异性和设计改进的TCR基疗法至关重要.
研究的目的:
- 确定与NRAS Q61K和Q61R新抗原和HLA-A1.1复合的人类TCR的晶体结构.
- 研究TCR对新抗原与野生类型NRAS的双重识别和特异性的基础分子机制.
- 评估AlphaFold AI模型在预测TCR--MHC复杂结构方面的实用性和准确性.
主要方法:
- 使用X射线晶体学来确定TCR-新抗原-MHC复合物的高分辨率结构.
- 多个AlphaFold版本,包括AlphaFold2 (TCRmodel2) 和AlphaFold3,被用来建模这些复杂物.
- 在实验确定结构和人工智能生成模型之间进行了比较分析.
主要成果:
- 晶体结构揭示了人类TCR的分子细节,该TCR识别了NRAS Q61K和Q61R新抗原结合HLA-A1.1.
- 这些结构阐明了TCR与野生类型NRAS相比的双重特异性的基础.
- 通过增强采样,AlphaFold2 (TCRmodel2) 成功模拟了TCR--MHC复合体,显示出强大的性能.
- AlphaFold3也显示出有前途的结果,尽管在不同的复合体中取得了不同的成功.
结论:
- 这项研究为TCR识别共享的癌症新抗原提供了关键的结构性见解.
- 该研究强调了使用AlphaFold用于建模TCR--MHC相互作用的潜在和实际考虑.
- 这些发现可以为基于TCR的免疫疗法的合理设计提供信息,这些免疫疗法针对特定的癌症新抗原.
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