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相关概念视频

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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相关实验视频

Updated: Jun 24, 2025

Analyzing Tumor and Tissue Distribution of Target Antigen Specific Therapeutic Antibody
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DeepSP:基于深度学习的空间特性来预测单克隆抗体的稳定性.

Lateefat Kalejaye1, I-En Wu1, Taylor Terry1

  • 1Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, NJ, United States.

Computational and structural biotechnology journal
|June 3, 2024
PubMed
概括
此摘要是机器生成的。

深度空间属性 (DeepSP) 是一种新的深度学习模型,可以从序列中预测抗体的空间属性,从而减少计算时间. 这通过预测粘度和聚合而加速治疗性抗体的发展,而无需复杂的模拟.

关键词:
抗体的稳定性 抗体的稳定性深度学习是一种深度学习.分子动力学模拟模型一个单克隆抗体的抗体.空间聚合倾向的空间聚合倾向空间充电地图的空间充电地图

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科学领域:

  • 生物技术和制药科学 生物技术和制药科学
  • 计算生物学和化学信息学

背景情况:

  • 治疗性抗体的发展受到高粘度和聚合问题的阻碍.
  • 空间电荷图 (SCM) 和空间聚合倾向 (SAP) 等预测性计算方法依赖于计算密集的分子动力学 (MD) 模拟.
  • 之前的深度学习模型,如DeepSCM,已经显示出从序列数据预测SCM的前景.

研究的目的:

  • 开发一个深度学习替代模型,DeepSP,可以从抗体序列直接预测SCM和SAP.
  • 评估DeepSP衍生特征对预测抗体聚合率的有用性.
  • 显著减少与预测抗体结构性质相关的计算负担.

主要方法:

  • 在20530个抗体序列的数据集上训练了一种卷积神经网络深度学习替代模型DeepSP.
  • DeepSP仅基于氨基酸序列来预测抗体变量区域的SCM和SAP得分.
  • 利用DeepSP衍生的描述符作为机器学习模型中的特征来预测抗体聚合率.

主要成果:

  • 在30个属性中,DeepSP在预测和MD衍生的得分之间实现了高的线性相关系数 (0.76-0.96,平均0.87).
  • 使用DeepSP功能的机器学习模型在预测抗体聚合率方面表现与基于MD的方法相当.
  • 与传统的MD模拟相比,DeepSP方法大大减少了计算时间.

结论:

  • DeepSP提供了一种快速而准确的方法,仅从序列数据生成抗体结构性质.
  • 这通过绕过漫长的MD模拟来加速治疗抗体的查和开发.
  • DeepSP为预测抗体稳定性和其他属性的机器学习模型提供了宝贵的基于序列的功能.