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相关概念视频

Cryo-electron Microscopy01:28

Cryo-electron Microscopy

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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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Protein Organization01:24

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
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相关实验视频

Updated: Jun 10, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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蛋白质复杂结构建模通过冷EM图和蛋白质序列之间的交叉模式对齐.

Sheng Chen1, Sen Zhang1, Xiaoyu Fang1

  • 1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.

Nature communications
|October 11, 2024
PubMed
概括

EModelX是一种使用冷电子显微镜 (cryo-EM) 数据进行蛋白质复杂建模的新型自动化方法. 它通过将冷电磁图与蛋白质序列对齐来准确地重建蛋白质结构,改进了现有的技术.

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Single Particle Cryo-Electron Microscopy: From Sample to Structure
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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:

  • 电子显微镜 (cryo-EM) 是确定蛋白质复杂结构的关键技术.
  • 现有的自动化建模方法往往由于链分离中的错误而失败,没有序列指导.
  • 噪音和冷EM图中的交链相互作用可能导致不准确的蛋白质结构模型.

研究的目的:

  • 从冷EM数据开发一种完全自动化的蛋白质复杂结构建模方法.
  • 为了提高基于冷EM的蛋白质结构确定的准确性和可靠性.
  • 为强大的冷电磁模型构建提供序列导向的方法.

主要方法:

  • EModelX使用多任务深度学习来预测Cα原子,骨干原子和氨基酸类型从冷EM地图.
  • 它使用冷电磁密度图和蛋白质序列之间的交叉模式对齐进行建模.
  • 序列引导的Cα线程用于填补空白并完善最终的结构模型.

主要成果:

  • 在恢复PDB沉积结构时,EModelX实现了1.17 Å的平均RMSD,证明了接近原子级的精度.
  • 该方法的最终模型达到0.808的平均TM得分,超过了最先进的技术.
  • 将EModelX与AlphaFold结合使用进一步改善了TM平均得分,达到0.911.

结论:

  • EModelX为冷-EM蛋白质复杂结构建模提供了一个强大的,自动化的解决方案.
  • 序列导向方法显著提高了模型的准确性和可靠性.
  • EModelX有可能改进现有的蛋白质数据库 (PDB) 结构,并推进结构生物学研究.