Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Cryo-electron Microscopy01:28

Cryo-electron Microscopy

3.2K
Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...
3.2K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.4K
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.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
2.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

SHREC 2025: Protein surface shape retrieval including electrostatic potential.

Computers & graphics·2026
Same author

DAQplugin: Deep Learning based Real-time Model Evaluation Plugin for ChimeraX.

bioRxiv : the preprint server for biology·2026
Same author

Direct Detection and Atomic Modeling of Ligands in Cryo-EM Maps Using Deep Learning.

bioRxiv : the preprint server for biology·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors.

Journal of cheminformatics·2026
Same author

Multivalent recognition of ferritin by full-length NCOA4 enables robust ferritinophagy.

Protein science : a publication of the Protein Society·2026

相关实验视频

Updated: Jun 7, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.2K

二次结构检测和结构建模用于冷电磁波.

Pranav Punuru1, Anika Jain1, Daisuke Kihara2,3

  • 1Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.

Methods in molecular biology (Clifton, N.J.)
|November 14, 2024
PubMed
概括

深度学习工具通过分析低温电子显微镜 (cryo-EM) 地图来帮助结构生物学. 这些人工智能模型有助于从低分辨率的冷EM数据中建模蛋白质结构,提高准确性.

关键词:
化阅读 化阅读 化阅读在 DAQQ 找 DAQQ深度学习是一种深度学习.这是一个DeepMainmast.在Emap2sec中使用.结构生物学是结构生物学.结构检测 结构检测结构建模 结构建模低温电磁波冷却器 (Cryo-EM) 是一个非常好的方法.

更多相关视频

Structure of HIV-1 Capsid Assemblies by Cryo-electron Microscopy and Iterative Helical Real-space Reconstruction
12:38

Structure of HIV-1 Capsid Assemblies by Cryo-electron Microscopy and Iterative Helical Real-space Reconstruction

Published on: August 9, 2011

17.4K
Single Particle Cryo-Electron Microscopy: From Sample to Structure
11:52

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

8.4K

相关实验视频

Last Updated: Jun 7, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.2K
Structure of HIV-1 Capsid Assemblies by Cryo-electron Microscopy and Iterative Helical Real-space Reconstruction
12:38

Structure of HIV-1 Capsid Assemblies by Cryo-electron Microscopy and Iterative Helical Real-space Reconstruction

Published on: August 9, 2011

17.4K
Single Particle Cryo-Electron Microscopy: From Sample to Structure
11:52

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

8.4K

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:

  • 低温电子显微镜 (cryo-EM) 能够在高分辨率下确定宏分子结构.
  • 在低于3 Å的分辨率下,手动对冷电磁密度图的原子建模具有挑战性.
  • 低分辨率的冷电磁图需要先进的方法来准确解释结构.

研究的目的:

  • 介绍一套深度学习工具,用于分析各种分辨率的冷电磁密度图.
  • 帮助结构生物学家从具有挑战性的低分辨率冷EM数据中建模蛋白质结构.
  • 提供可访问的计算工具,用于冷EM数据的解释.

主要方法:

  • 开发深度学习算法,用于识别冷电磁图中的结构特征.
  • DeepMainmast:一种自动化工具,用于从近原子分辨率 (≤5 Å) 的冷电磁图片中建模全原子结构.
  • Emap2sec和Emap2sec+:用于检测中等分辨率 (5-10 Å) 地图中的蛋白质二次结构和核酸的工具.
  • 用于量化地图模型匹配和识别潜在的建模错误的DAQ分数.

主要成果:

  • 深度学习成功地识别了氨基酸和原子的局部地图特征.
  • DeepMainmast从高分辨率的冷电磁图中自动建模蛋白质主链.
  • 在中等分辨率的地图中,Emap2sec和Emap2sec+可以准确地检测二次结构和核酸.
  • DAQ评分有效地评估了冷-EM地图模型相关性的质量.

结论:

  • 基于深度学习的工具显著提高了冷电磁密度图的分析,特别是在较低分辨率.
  • 开发的工具套件,包括DeepMainmast和Emap2sec,为结构生物学家提供了宝贵的帮助.
  • 这些计算资源可以通过Web服务器访问,促进结构生物学研究的更广泛采用.