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相关概念视频

Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

558
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...
558
T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

694
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...
694

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相关实验视频

Updated: Jun 19, 2025

Streamlined Single Cell TCR Isolation and Generation of Retroviral Vectors for In Vitro and In Vivo Expression of Human TCRs
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TSpred:使用配对链TCR序列数据进行TCR-表位相互作用的强大预测框架.

Ha Young Kim1, Sungsik Kim2, Woong-Yang Park2,3,4

  • 1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea.

Bioinformatics (Oxford, England)
|July 25, 2024
PubMed
概括

一种新的深度学习方法TSpred准确地预测T细胞受体 (TCR) -表位相互作用. 这种方法通过改善对新型表位体的TCR结合特异性预测来增强癌症免疫疗法和疫苗设计.

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Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay
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Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
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Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
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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:

  • 预测T细胞受体 (TCR) -表位相互作用对于推进癌症免疫疗法和疫苗开发至关重要.
  • 由于数据有限,准确的预测是具有挑战性的,特别是对于新型表征.

研究的目的:

  • 引入TSpred,这是一个用于泛特定TCR绑定特异性预测的深度学习模型.
  • 开发一个强大的模型,能够对未见的表征进行概括.

主要方法:

  • TSpred使用了结合卷积神经网络 (CNN) 和注意力机制的深度学习架构.
  • 采用互惠注意力机制来识别TCR和表观体之间的关键相互作用模式.
  • 该模型在配对链TCR数据上进行训练.

主要成果:

  • TSpred在预测已知和新型表征的TCR特异性方面取得了最先进的性能.
  • 该模型证明了对数据集中存在的不平衡偏差的稳定性.
  • 互惠注意力机制通过突出重要约束区域来提供可解释性.

结论:

  • TSpred 是一种可靠和强大的方法,用于TCR-Epitope结合的预测.
  • 该方法为免疫学应用提供了更好的准确性和可解释性.
  • 开发的模型推动了预测免疫学领域的发展.