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

2.3K
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...
2.3K

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: May 30, 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

基于人工智能的生物分子结构建模方法用于Cryo-EM.

Farhanaz Farheen1, Genki Terashi2, Han Zhu1

  • 1Department of Computer Science, Purdue University, West Lafayette, IN, USA.

Current opinion in structural biology
|January 26, 2025
PubMed
概括

人工智能 (AI) 增强了冷电子显微镜 (Cryo-EM) 数据处理,以确定复杂的宏分子结构. 这篇评论强调了人工智能.

关键词:
在这里,我们可以看到AIAIAI.人工智能的人工智能是人工智能.化电磁波是一种冷电磁波.深度学习是一种深度学习.结构的异质性 结构的异质性结构建模 结构建模结构验证 结构验证

更多相关视频

A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion
13:43

A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion

Published on: January 31, 2022

12.9K
Do's and Don'ts of Cryo-electron Microscopy: A Primer on Sample Preparation and High Quality Data Collection for Macromolecular 3D Reconstruction
09:25

Do's and Don'ts of Cryo-electron Microscopy: A Primer on Sample Preparation and High Quality Data Collection for Macromolecular 3D Reconstruction

Published on: January 9, 2015

46.0K

相关实验视频

Last Updated: May 30, 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
A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion
13:43

A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion

Published on: January 31, 2022

12.9K
Do's and Don'ts of Cryo-electron Microscopy: A Primer on Sample Preparation and High Quality Data Collection for Macromolecular 3D Reconstruction
09:25

Do's and Don'ts of Cryo-electron Microscopy: A Primer on Sample Preparation and High Quality Data Collection for Macromolecular 3D Reconstruction

Published on: January 9, 2015

46.0K

科学领域:

  • 结构生物学是结构生物学.
  • 生物物理学的生物物理.
  • 计算生物学是一种计算生物学.

背景情况:

  • 电子显微镜 (Cryo-EM) 是一种强大的技术,用于确定生物巨分子的高分辨率结构.
  • 传统的Cryo-EM数据处理涉及复杂的计算步骤,通常限制吞吐量和分辨率.
  • 人工智能 (AI) 的进步,特别是深度学习,为优化 Cryo-EM 工作流提供了新的可能性.

研究的目的:

  • 审查应用到关键的Cryo-EM数据处理步骤的基于人工智能的最先进技术.
  • 突出人工智能对改进宏分子结构建模和异质性分析的影响.
  • 提供对当前AI在冷EM领域的现状和未来方向的见解.

主要方法:

  • 关于人工智能应用在冷电磁数据处理中的最新文献的综述.
  • 专注于用于图像处理,粒子选择和3D重建的深度学习算法.
  • 讨论AI驱动的方法来分析数据集内的结构异质性.

主要成果:

  • 人工智能显著提高了各种 Cryo-EM 数据处理步骤的准确性和效率.
  • 深度学习模型在提高粒子识别和3D模型质量方面特别有前途.
  • 人工智能有助于对宏分子复合体中的构造异质性进行更强有力的分析.

结论:

  • 人工智能正在通过加速结构确定和分析以前难以处理的生物系统来改变冷EM.
  • 人工智能工具的持续开发和整合对于推进结构生物学至关重要.
  • 由人工智能驱动的Cryo-EM准备通过高分辨率的结构确定解锁新的生物学见解.