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相关概念视频

Hybridoma Technology01:31

Hybridoma Technology

14.9K
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 Structure01:10

Antibody Structure

60.3K
Overview
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...
60.3K
Antibody Structure and Classes01:25

Antibody Structure and Classes

957
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.
957

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相关实验视频

Updated: Jul 19, 2025

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application
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Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

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使用机器学习简化复杂的抗体工程.

Emily K Makowski1, Hsin-Ting Chen2, Peter M Tessier3

  • 1Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA.

Cell systems
|August 17, 2023
PubMed
概括
此摘要是机器生成的。

机器学习通过预测具有所需性质的抗体变体来加速抗体工程. 这种方法减少了开发稳定和高亲缘关系单克隆抗体的实验努力.

关键词:
这是一个CDRCDR.在IgG IgG的基础上.亲和关系是一种亲和关系.抗原是一种抗原.确定互补性的地区.深度学习是一种深度学习.指导进化是指导进化的.mAbAb 在线观看蛋白质设计 蛋白质设计稳定的稳定性 稳定的稳定性地区变量 地区变量

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Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
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Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

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Generation of Discriminative Human Monoclonal Antibodies from Rare Antigen-specific B Cells Circulating in Blood
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Generation of Discriminative Human Monoclonal Antibodies from Rare Antigen-specific B Cells Circulating in Blood

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Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
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科学领域:

  • 生物技术是生物技术.
  • 计算生物学 计算生物学
  • 免疫学 免疫学 免疫学

背景情况:

  • 机器学习 (ML) 正在彻底改变抗体工程.
  • ML可以有效地产生类似药物的单克隆抗体.
  • 预测模型可以减少实验工作量.

研究的目的:

  • 审查针对抗体工程的ML最近的进展.
  • 讨论ML对抗体发现和开发的影响.
  • 识别ML方法的挑战和机会.

主要方法:

  • 无监督的ML算法用于预测固有的抗体特性 (例如稳定性).
  • 在深度测序数据上训练的监督的ML算法对外部特性 (例如亲和力) 进行深度测序.
  • 对大型蛋白序列和抗体库数据集的分析.

主要成果:

  • ML预测抗体变体具有与原生相似的内在特性,减少实验.
  • 在没有额外选的情况下,ML可以预测具有所需外部特性的变体.
  • 对抗体工程的效率和成功率取得了显著的进步.

结论:

  • 机器学习是抗体工程中的一个改变范式的工具.
  • ML增强了功能性抗体候选人的预测.
  • 未来的机遇在于完善ML模型并应对挑战.