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相关概念视频

Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

393
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...
393

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相关实验视频

Updated: May 15, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
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AMULETY:一个Python包,可以嵌入自适应性免疫受体序列.

Meng Wang1, Yuval Kluger1,2,3, Steven H Kleinstein1,2,4

  • 1Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT, USA.

bioRxiv : the preprint server for biology
|April 8, 2025
PubMed
概括
此摘要是机器生成的。

AMULETY是一个新的Python工具,它简化了使用自适应性免疫受体语言模型. 它为免疫受体序列生成嵌入,使研究人员更容易获得高级分析.

关键词:
B细胞受体受体 B细胞受体受体计算免疫学计算免疫学嵌入式 嵌入式嵌入式机器学习是机器学习.下一代测序的下一代测序.

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相关实验视频

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科学领域:

  • 免疫信息学是指免疫信息学.
  • 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:

  • 大型语言模型 (LLM) 显示出分析适应性免疫受体的前景.
  • 存在多种不同的LLM,但它们的多样化可访问性阻碍了在免疫学研究中的广泛应用.
  • 标准化LLM的使用对于充分利用其在自适应性免疫受体序列分析中的全部潜力至关重要.

研究的目的:

  • 介绍AMULETY,这是一个Python软件包,用于生成适应性免疫受体序列的语言模型嵌入.
  • 为访问和利用各种预训练的蛋白质和抗体语言模型提供一个用户友好的界面.
  • 通过简化对免疫受体数据的LLM衍生功能的集成来促进下游应用.

主要方法:

  • 开发了AMULETY作为一个基于Python的软件包.
  • 实现了功能,以生成适应性免疫受体的氨基酸序列的嵌入.
  • 支持嵌入生成使用预训练的蛋白质或抗体语言模型,用于配对和单一的免疫受体链.

主要成果:

  • 证明AMULETY能够产生适应性免疫受体序列的嵌入.
  • 展示了使用SARS-CoV-2表位结合器数据的不同语言模型生成的嵌入空间的变化.
  • 强调了不同的LLM捕获了与抗体-表皮质相互作用相关的不同生物特征.

结论:

  • AMULETY提高了适应性免疫受体序列的LLM嵌入的可访问性和可用性.
  • 该工具使研究人员能够在没有复杂的设置的情况下利用各种LLM,从而促进更广泛的采用.
  • AMULETY促进了对免疫受体谱和抗体-抗原相互作用的细微分析.