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Electron Microscope Tomography and Single-particle Reconstruction01:07

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
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A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
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相关实验视频

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Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy
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广泛的角取样使得电子断层图像中宏分子的敏感定位成为可能.

Marten L Chaillet1, Gijs van der Schot1, Ilja Gubins2

  • 1Structural Biochemistry, Bijvoet Centre for Biomolecular Research, Utrecht University, 3584 CG Utrecht, The Netherlands.

International journal of molecular sciences
|September 9, 2023
PubMed
概括

本研究在PyTOM中引入了一种GPU加速模板匹配 (TM) 方法,使得在冷电子断层扫描中更快地检测大分子. 这一进步允许更彻底的搜索,提高了识别细胞结构 (如核糖体) 的灵敏度和准确性.

关键词:
在 GPU 加速加速.电子冷断层扫描 (电子冷断层扫描).粒子的定位和识别.模板匹配的匹配方式销量登记 销量登记 销量登记 销量登记

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相关实验视频

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科学领域:

  • 结构生物学 结构生物学
  • 生物物理学的生物物理.
  • 计算生物学 计算生物学

背景情况:

  • 低温电子断层扫描 (cryo-ET) 可视化细胞环境中的宏分子.
  • 模板匹配 (TM) 对于检测这些宏分子至关重要,但在广泛的旋转搜索中受到计算成本的限制.
  • 以前的方法缺乏计算能力来探索粒子检测的全部旋转空间.

研究的目的:

  • 在PyTOM软件包中开发和实施一个GPU加速模板匹配 (TM) 算法.
  • 调查广泛的角取样对冷ET中的粒子检测灵敏度和错误发现率的影响.
  • 通过改进宏分子识别和分类,增强子图平均化工作流程.

主要方法:

  • 为 PyTOM 开发了一个模板匹配 (TM) 的 GPU 实现.
  • 在Crowther标准之外进行了广泛的角度搜索.
  • 量化灵敏度和错误发现率,以核糖体识别和检测为例.
  • 整合了增强的TM与子镜平均化工作流程.

主要成果:

  • 该GPU实现大大加快了TM的定向搜索.
  • 在可行的时间框架内实现超出Crowther标准的采样.
  • 证明了对宏分子检测的敏感性增加和错误发现率降低.
  • 展示了TM对局部倾斜系列对齐和离子束削损伤的敏感性.
  • 自动化核糖体分类实现了高灵敏度和低错误发现率.

结论:

  • 在PyTOM中的GPU加速TM显著增强了冷ET中的宏分子检测.
  • 广泛的角取样提高了准确性,并揭示了结构细节.
  • 该方法有助于直接用于高分辨率的平均值和分析复杂的组织,如多元体.