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Cryo-electron Microscopy

Cryo-electron Microscopy

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...

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Updated: Jul 28, 2025

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

在冷电子显微镜模型中的残留级误差检测.

Gabriella Reggiano¹, Wolfgang Lugmayr², Daniel Farrell³

¹Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA.

Structure (London, England : 1993)

|May 30, 2023

概括

此摘要是机器生成的。

医学是一个新的工具,可以在嵌入到冷电子显微镜 (cryo-EM) 地图中的蛋白质模型中发现错误. 这种方法提高了蛋白质结构的精度,有助于结构生物学家.

关键词:

低温电子显微镜的使用方法机器学习是机器学习.模型建筑模型建筑蛋白质模型验证的有效性

更多相关视频

Cryo-EM and Single-Particle Analysis with Scipion

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Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope

Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope

Published on: March 16, 2022

相关实验视频

Last Updated: Jul 28, 2025

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

Cryo-EM and Single-Particle Analysis with Scipion

Cryo-EM and Single-Particle Analysis with Scipion

Published on: May 29, 2021

Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope

Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope

Published on: March 16, 2022

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:

从中等分辨率 (3-5 Å) 电子显微镜 (cryo-EM) 地图构建精确的蛋白质模型具有重大挑战,并且容易出现错误.
现有的方法可能很难在这些复杂的结构模型中识别局部不准确性.

研究的目的:

开发和验证一个强大的计算工具,用于识别和纠正蛋白质结构中的本地骨干错误,并将其建模成冷EM地图.
通过使用冷EM数据提高蛋白质结构确定的可靠性和准确性.

主要方法:

开发了MEDIC (Model Error Detection in Cryo-EM),一个统计模型,将本地适合密度指标与深度学习衍生的结构信息集成在一起.
在28个蛋白质结构上验证了MEDIC,随后在更高分辨率下解决.
应用MEDIC来纠正沉积的冷电磁结构中的错误,并识别AlphaFold预测中的错误.

主要成果:

在识别低分辨率和高分辨率结构之间的差异方面,MEDIC实现了68%的精度和60%的回忆.
在12个沉积的冷EM结构中成功纠正了100多个错误.
在4个精细的AlphaFold预测中识别出了80%精度和60%回忆率的错误.

结论:

对于结构生物学家来说,MEDIC是一个强大而通用的工具,能够检测通过手工构建和深度学习方法生成的蛋白质模型中的错误.
该模型增强了冷-EM衍生蛋白质结构的质量控制过程.
促进更准确的蛋白质结构改进和重建,特别是随着深度学习预测越来越融入工作流程.