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相关概念视频

Affinity and Avidity01:41

Affinity and Avidity

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Hybridoma Technology01:31

Hybridoma Technology

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Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation,...
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Antibody Structure and Classes01:25

Antibody Structure and Classes

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Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.
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Antibody Structure01:10

Antibody Structure

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Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...
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相关实验视频

Updated: Jun 14, 2025

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
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可预训练的几何图形神经网络用于抗体亲和力成熟.

Huiyu Cai1,2,3, Zuobai Zhang2,3, Mingkai Wang4,5

  • 1BioGeometry, Beijing, China.

Nature communications
|September 6, 2024
PubMed
概括

这项研究介绍了GearBind,这是一种新的几何深度学习模型,用于增强抗体结合亲和力. GearBind成功地改善了在体中的抗体-抗原相互作用,证明了其在抗体治疗开发方面的潜力.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 结构生物学是结构生物学.
  • 机器学习 机器学习

背景情况:

  • 抗体治疗的开发需要优化抗体-抗原结合亲和力.
  • 目前用于亲和力成熟的计算方法有局限性.

研究的目的:

  • 为了介绍GearBind,一个可预训练的几何图形神经网络,用于in silico抗体亲和力成熟.
  • 用最先进的方法来评估GearBind的性能.

主要方法:

  • 利用多关系图形构造和多层次的几何信息传递.
  • 在大规模未标记的蛋白质结构数据上使用对比预训练.
  • 基于GearBind开发了一个集体模型,用于增强抗体结合.

主要成果:

  • 在基准数据集 (SKEMPI) 和独立测试集上,GearBind的表现优于现有的方法.
  • 合奏模型成功地增强了两个不同的抗体的结合亲和力.
  • 在设计突变者中,ELISA EC50降低了17倍,KD值降低了6.1倍.

结论:

  • 几何深度学习和有效的预训练是建模宏分子相互作用的强大工具.
  • 在加速抗体疗法设计和开发方面,GearBind显示出显著的前景.