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相关概念视频

Mass Photometry to Study Antigen-Antibody Interactions on a Single-Molecule Level05:16

Mass Photometry to Study Antigen-Antibody Interactions on a Single-Molecule Level

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In this video, we demonstrate the use of mass photometry to study antigen-antibody interactions in a flow chamber. The focused light scatters differently based on the mass of the molecules. A detector collects the scattered light and generates the interference pattern as spots of varied intensities, which correlate with the molecular...
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Rapid Determination of Antibody-Antigen Affinity by Mass Photometry10:17

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We describe a single-molecule approach to antigen-antibody affinity measurements using mass photometry (MP). The MP-based protocol is fast, accurate, uses a very small amount of material, and does not require protein...
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Analysis of Protein Complex Formation at Micromolar Concentrations by Coupling Microfluidics with Mass Photometry06:39

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This protocol combines mass photometry with a novel microfluidics system to investigate low-affinity protein-protein interactions. This approach is based on the rapid dilution of highly concentrated complexes in solution, which enables low-affinity measurements and broadens the applicability of mass...
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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools07:01

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Mass spectrometry-based proteomic data is available in open databases and accessible using free tools. Given the complexity of database searches and descriptions, many biologists lack the knowledge to utilize these datasets. Here, we provide a guide on using free tools for basic proteomic data...
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This article describes the use of a software application, mAbScale, for the calculation of masses for monoclonal antibody-based protein...
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A novel method of sample preparation was developed to accommodate cell and tissue coculture to detect small molecule exchange using imaging mass...
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相关实验视频

Updated: Jan 20, 2026

Mass Photometry to Study Antigen-Antibody Interactions on a Single-Molecule Level
05:16

Mass Photometry to Study Antigen-Antibody Interactions on a Single-Molecule Level

773

基于深度学习的质量摄影数据事件分类,以在单分子水平上进行最佳质量测量.

Kishwar Iqbal1,2, Jan Christoph Thiele1,2, Dominik Saman1,2

  • 1The Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, U.K.

ACS nano
|January 19, 2026
PubMed
概括
此摘要是机器生成的。

质量光度测量 (MP) 测量通过一种新的AI方法来改进,该方法分析单分子动态. 这种方法提高了质量分辨率和灵敏度,从而导致更准确的生物分子研究.

关键词:
3D卷积神经网络是一个3D卷积神经网络.质量测光仪的质量测光仪.测量反对测量的反解决电源的解决能力时间空间信息.

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相关实验视频

Last Updated: Jan 20, 2026

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科学领域:

  • 生物物理学的生物物理.
  • 生物化学 生物化学
  • 计算生物学 计算生物学

背景情况:

  • 质量光度 (MP) 是一种基于溶液的技术,用于分析生物分子相互作用和动态.
  • 当前的MP方法随着时间的推移平均信号,这些信号可能会受到粒子运动的影响,限制测量准确度.
  • 提高质量分辨率,灵敏度和MP中的度确定对于详细的生物分子分析至关重要.

研究的目的:

  • 开发一种人工智能驱动的方法,以提高质量单分子测量量质量测量中的质量.
  • 通过分析它们的时空动态来隔离最佳的单分子事件,从而减少人工制造物.
  • 提高质量光度测量的分辨率和可靠性,特别是在具有挑战性的实验条件下.

主要方法:

  • 通过监督学习开发和训练了一个三维卷积残余网络.
  • 该网络分析粒子着陆事件的3D"缩略图",以分类它们的时空动态.
  • 这种方法有效地将高质量的单分子测量与噪音数据隔离起来.

主要成果:

  • 人工智能方法成功地隔离了最佳的单分子测量,减少了累积的基因图形文物.
  • 与传统方法相比,解析功率提高了多达2倍.
  • 该方法在不同样本属性和条件的各种实验数据集中显示出强大的性能.

结论:

  • 这种人工智能驱动的方法显著提高了质量摄影测量测量的质量和可靠性.
  • 它使高质量的MP数据采集成为可能,即使是在具有低最佳条件的具有挑战性的实验场景中.
  • 该方法提供数据驱动的反,以促进改进的MP实验设计和执行.