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

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

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

Knowledge-based systems·2026
Same author

Peptide-protein docking: from physics-based models to generative intelligence.

Chemical communications (Cambridge, England)·2026
Same author

Accurate Macromolecular Complex Modeling for Cryo-EM with CryoZeta.

bioRxiv : the preprint server for biology·2026
Same journal

TDP-43 proteinopathy as a biomarker and therapeutic target in amyotrophic lateral sclerosis.

Biochemical Society transactions·2026
Same journal

Advancing the monitoring of organelle contact sites in vitro and in vivo.

Biochemical Society transactions·2026
Same journal

Mechanisms influencing transient cytoplasmic protein targeting to intracellular lipid droplets.

Biochemical Society transactions·2026
Same journal

Replication associated nuclear DNA mismatch repair across kingdoms.

Biochemical Society transactions·2026
Same journal

Phosphatases of regenerating liver downregulate PTEN to promote tumorigenesis.

Biochemical Society transactions·2026
Same journal

Implications of Rho GTPase signaling in cancer immunotherapy.

Biochemical Society transactions·2026
查看所有相关文章

相关实验视频

Updated: May 28, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.5K

通过使用深度学习的冷电磁图进行先进的结构建模.

Shu Li1, Genki Terashi2, Zicong Zhang1

  • 1Department of Computer Science, Purdue University, West Lafayette, IN, U.S.A.

Biochemical Society transactions
|February 10, 2025
PubMed
概括
此摘要是机器生成的。

从冷电子显微镜 (cryo-EM) 地图进行自动结构建模对于结构生物学至关重要. 本综述涵盖了新的和合适的方法,强调AI.

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.低温电磁波冷却器 (Cryo-EM) 是一个非常好的方法.深度学习是一种深度学习.结构建模 结构建模结构验证 结构验证

更多相关视频

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
Author Spotlight: Enhancing CryoEM Sample Preparation Using Graphene Monolayer on Microscopy Grids
07:57

Author Spotlight: Enhancing CryoEM Sample Preparation Using Graphene Monolayer on Microscopy Grids

Published on: November 10, 2023

1.7K

相关实验视频

Last Updated: May 28, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.5K
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
Author Spotlight: Enhancing CryoEM Sample Preparation Using Graphene Monolayer on Microscopy Grids
07:57

Author Spotlight: Enhancing CryoEM Sample Preparation Using Graphene Monolayer on Microscopy Grids

Published on: November 10, 2023

1.7K

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:

  • 电子显微镜 (cryo-EM) 已经改变了结构生物学,使得高分辨率的生物分子结构确定成为可能.
  • 准确地解释冷电磁密度图对于理解分子机制至关重要.
  • 传统方法在建模复杂或低分辨率结构时面临挑战.

研究的目的:

  • 从冷电磁密度图提供自动结构建模的简要概述.
  • 为了分类当前的建模方法.
  • 突出人工智能 (AI) 和深度学习在现场的影响.

主要方法:

  • 将建模方法分为de novo方法 (对于高分辨率地图>5 Å) 和拟合方法 (对于低分辨率地图<5 Å) 的分类.
  • 讨论深度学习和人工智能驱动的技术,应用于冷EM结构建模.
  • 审查自动化建模工具的演变和当前状态.

主要成果:

  • 基于地图分辨率的自动结构建模方法出现了两个主要类别.
  • 深度学习和人工智能方法正在显著提高冷EM模型构建的准确性和效率.
  • 这些由人工智能驱动的方法在解释复杂的冷电磁数据方面被证明是具有变革性的.

结论:

  • 自动结构建模是冷EM数据分析的一个关键步骤.
  • 人工智能和深度学习的整合正在彻底改变这个领域,使得结构的确定更强大,更准确.
  • 未来的方向包括进一步开发人工智能驱动的工具,用于增强的冷EM地图解释.