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相关概念视频

Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

5.4K
Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

2.6K
Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
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相关实验视频

Updated: Jan 7, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

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约束脱的隐性扩散用于蛋白质的背面映射.

Xu Han1,2,3, Yuancheng Sun1,2,3, Kai Chen3

  • 1University of Chinese Academy of Sciences, Beijing 100190, China.

Journal of chemical theory and computation
|December 27, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了CODLAD,一种用于蛋白质结构重建的新方法. 它从粗的模拟中高效地生成准确和多样化的全原子模型,克服了现有的后映射技术的局限性.

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10:20

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Single-Molecule Diffusion and Assembly on Polymer-Crowded Lipid Membranes
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科学领域:

  • 计算生物学 计算生物学
  • 结构生物学 结构生物学
  • 生物物理学的生物物理.

背景情况:

  • 粗粒度 (CG) 模拟提供了高效的蛋白质构造探索.
  • 从CG模型 (后映射) 中重建所有原子细节至关重要,但具有挑战性.
  • 现有的逆向映射方法难以平衡准确性和形状多样性.

研究的目的:

  • 引入一种新的两阶段框架,CODLAD (COnstraint 脱 LAtent 扩散),用于改进蛋白质结构的逆向映射.
  • 为了提高效率和准确性,将约束处理与生成过程分开.
  • 从CG模型中产生结构有效和多样化的全原子蛋白质构造.

主要方法:

  • 开发了一个两阶段的框架:隐性压缩和隐性扩散.
  • 将压缩的原子结构压缩成离散的潜在表示,嵌入约束.
  • 用于在潜空间中消除扩散,以有效生成全原子结构.

主要成果:

  • 在原子精度方面,CODLAD实现了最先进的性能.
  • 与现有方法相比,该方法显示出优越的形状多样性.
  • 评估显示了高计算效率和在各种蛋白质数据集中强大的概括性.

结论:

  • CODLAD有效地解决了当前后映射技术中的权衡问题.
  • 该框架能够准确多样化地重建蛋白质结构.
  • CODLAD代表了计算蛋白质建模的重大进步.