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

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

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

bioRxiv : the preprint server for biology·2026
Same author

A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across species.

Nature methods·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

Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC.

Nature communications·2026
Same author

MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.

Knowledge-based systems·2026
Same journal

ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.

Nature methods·2026
Same journal

RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.

Nature methods·2026
Same journal

Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution.

Nature methods·2026
Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026
Same journal

Cloud-based microscope enables live neuroimaging for 24 h and beyond with worldwide access.

Nature methods·2026
Same journal

Deep molecular profiling in three dimensions.

Nature methods·2026
查看所有相关文章

相关实验视频

Updated: Jun 9, 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

DiffModeler:使用扩散模型为冷电磁图绘制大型宏分子结构建模.

Xiao Wang1, Han Zhu1, Genki Terashi2

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

Nature methods
|October 21, 2024
PubMed
概括
此摘要是机器生成的。

DiffModeler是一种新的自动化方法,用于使用冷电子显微镜 (cryo-EM) 数据建模大型蛋白质复杂结构. 它显著优于现有的方法,即使在低分辨率下,也能更好地确定复杂生物分子的结构.

更多相关视频

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

46.1K
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 9, 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
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

46.1K
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) 对于确定大型蛋白质复合物的结构至关重要.
  • 从冷EM数据中建模大型复合体 (>10个链) 是一个挑战,尤其是在较低分辨率下.

研究的目的:

  • 介绍DiffModeler,一种完全自动化的计算方法,用于从冷EM图表中建模大型蛋白质复杂结构.
  • 为了证明DiffModeler在各种分辨率的有效性,包括低分辨率数据.

主要方法:

  • DiffModeler使用扩散模型进行准确的骨干追踪.
  • 它集成了AlphaFold2预测的单链结构,用于精确的模型拟合.
  • 该方法在冷电磁数据集上进行了评估,分辨率从0-20 Å.

主要成果:

  • DiffModeler获得了高的模板建模得分:0.88和0.91为0-5 Å地图,0.92为5-10 Å地图.
  • 该方法与现有技术相比,表现明显改善.
  • 在低分辨率 (10-20 Å) 上证实了多功能性能,显示了可信的结果.

结论:

  • DiffModeler提供了一种强大的自动化解决方案,用于从冷EM数据中建模大型蛋白质复合体.
  • 它能够在一系列分辨率上表现出色,包括低分辨率地图,这提高了它在结构生物学中的实用性.
  • 这种方法推进了复杂生物组件的结构确定领域.