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相关概念视频

Cryo-electron Microscopy01:28

Cryo-electron Microscopy

3.6K
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...
3.6K

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Updated: Sep 11, 2025

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

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使用类似AlphaFold3的模型和分子动态的Cryo-EM配体构建.

Nandan Haloi1, Rebecca J Howard1,2, Erik Lindahl1,2

  • 1Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.

PLoS computational biology
|August 11, 2025
PubMed
概括

本研究介绍了一种人工智能驱动的方法,使用冷电子显微镜 (cryo-EM) 地图精确建模与蛋白质结合的小分子. 这种方法增强了冷EM数据中的联结体适配,改善了蛋白质-联结体相互作用的结构分析.

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) 可实现高分辨率的生物分子结构确定.
  • 在冷EM图中精确建模小分子连接体仍然具有挑战性.
  • 现有的建模方法往往以蛋白质为中心,限制了它们的应用到不同的连接体.

研究的目的:

  • 开发和验证一种集成方法,用于将小分子安装到冷EM密度图中.
  • 为了提高通过冷EM确定的连接体-蛋白质复杂结构的精度和分辨率.
  • 在冷EM研究中为模拟各种小分子提供强大的管道.

主要方法:

  • 人工智能 (AI) 与冷电磁密度引导模拟的整合.
  • 使用蛋白质序列,配体信息和实验冷电磁图作为输入.
  • 在分子动力学模拟中采用灵活的配合来改进连接体的位置.

主要成果:

  • 在各种蛋白质-连接体复合体 (激酶,GPCR,溶解物转运体) 上成功验证.
  • 在联体模型与地图交叉相关性显著改善,从40-71%到82-95%与模拟.
  • 演示了AI能够预测实验性联结体姿势的能力,模拟进一步提高了准确性.

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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Last Updated: Sep 11, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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Published on: July 19, 2024

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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

527

结论:

  • 开发的管道提供了一种简单的方法,用于在冷EM中建模配体-蛋白质复合体.
  • 这种方法提高了小分子相互作用的结构数据的分辨率和可靠性.
  • 通过原子水平的洞察力,更深入地了解小分子如何调节生物功能.