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相关概念视频

Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

481
Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
Before encountering any antigen, lymphocytes express these receptors. On B cells, the antigen receptor is a membrane-bound antibody molecule called BCR; on T cells, it is a T cell receptor or TCR. B and T cell receptors are composed of two...
481
T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

623
T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...
623

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相关实验视频

Updated: May 24, 2025

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells
13:58

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格拉特克:用数据效率高的预训练模型生成位特异性T细胞受体序列.

Zhenghong Zhou, Junwei Chen, Shenggeng Lin

    IEEE journal of biomedical and health informatics
    |March 3, 2025
    PubMed
    概括

    GRATCR产生了用于癌症免疫治疗的新型T细胞受体 (TCR) 序列. 这种深度学习框架增强了表位特异性和生物功能,以更少的数据优于现有模型.

    科学领域:

    • 免疫学 免疫学 免疫学
    • 计算生物学 计算生物学
    • 生物信息学是一种生物信息学.

    背景情况:

    • 针对癌细胞的免疫疗法中,T细胞受体 (TCRs) 是至关重要的.
    • 获取和优化TCR是实验密集和耗时的.
    • 深度生成模型显示了功能蛋白序列生成的前景.

    研究的目的:

    • 开发一个新的框架,GRATCR,用于针对特定表位的TCR序列的de novo生成.
    • 为了利用一个独特的杆.
    • 接种方式 接种方式
    • 战略结合了两个预先训练的模块.

    主要方法:

    • 使用了一个名为GRATCR.的深度生成模型框架.
    • 雇佣了一个小说.
    • 接种方式 接种方式
    • 战略是整合两个预先训练的模块.
    • 生成的TCR序列针对特定的表位.

    主要成果:

    • 与最先进的模型相比,GRATCR生成的TCR对目标表位体具有更高的特异性.
    • 生成的TCR显示出增强的生物功能.

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  • 该模型使用显著较少的培训数据实现了这些结果.
  • 产生了新的TCR序列,与自然序列不同.
  • 解释性分析证实了该模型能够捕捉关键的绑定模式.

    • 结论:

    • GRATCR提供了一种高效和有效的方法,用于产生高特异性,功能性的TCR序列用于免疫治疗.
    • 该框架减少了对广泛的湿实验室实验的依赖.
    • 在TCR设计的计算方法中,GRATCR代表了显著的进步.