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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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相关实验视频

Updated: Jan 12, 2026

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

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

Published on: July 19, 2024

用基于AlphaFold2的模型和密度引导模拟在替代状态下建模冷EM结构.

Tatiana Shugaeva¹, Rebecca J Howard^1,2, Nandan Haloi³

¹Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, SE-17165, Sweden.

Communications chemistry

|October 31, 2025

概括

这项研究引入了一种结合人工智能和模拟来建模复杂蛋白质结构的新方法,提高了具有多个功能状态的膜蛋白的精度.

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Cryo-EM and Single-Particle Analysis with Scipion

Cryo-EM and Single-Particle Analysis with Scipion

Published on: May 29, 2021

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Single Particle Cryo-Electron Microscopy: From Sample to Structure

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相关实验视频

Last Updated: Jan 12, 2026

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

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

Published on: July 19, 2024

Cryo-EM and Single-Particle Analysis with Scipion

Cryo-EM and Single-Particle Analysis with Scipion

Published on: May 29, 2021

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

科学领域:

结构生物学结构生物学
计算生物学计算生物学
生物物理学的生物物理.

背景情况:

精确的原子建模成冷电子显微镜 (cryo-EM) 地图对于蛋白质结构的确定至关重要.
具有多个功能状态的蛋白质的建模,特别是膜蛋白,由于结构灵活性和有限的模板可用性而具有挑战性.
低冷EM图像分辨率阻碍了替代蛋白质构造的新模型构建.

研究的目的:

开发和验证一种新的计算方法,用于将原子模型精制成冷电磁图,特别是对于表现出构造转换的蛋白质.
为了提高多个功能状态的膜蛋白的结构建模的准确性,传统方法不足.

主要方法:

使用AlphaFold2的多个序列对齐 (MSA) 空间的随机子样本生成多个初始蛋白质模型.
使用基于结构的k-means对生成的模型进行集群,以识别不同的构造状态.
从代表性集群模型进行密度引导分子动力学 (MD) 模拟.
选择最终的原子模型基于冷EM地图的合适性和整体模型质量.

主要成果:

与单一起点方法相比,拟议的精细化方法显著提高了适配精度.
对于经过构造变化的三种药理学相关的膜蛋白 (素受体类似受体,L型氨基酸载体,氨酸-氨酸-氨酸载体) 证明了提高准确性.
成功促进了这些膜蛋白的替代功能状态的构建.

结论:

使用生成性人工智能 (AI) 结合基于模拟的精细化组合构建是建模替代蛋白质状态的强大策略.
这种方法对于了解膜蛋白和其他复杂生物系统的功能动态尤其有益.
该方法为冷EM研究中的形状异质性所带来的结构确定挑战提供了可靠的解决方案.