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相关概念视频

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.4K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
11.4K
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

2.3K
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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Molecular Models02:00

Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Protein-protein Interfaces02:04

Protein-protein Interfaces

13.3K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
13.3K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

3.8K
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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相关实验视频

Updated: Sep 14, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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通过机器学习的可转移粗粒度模型来导航蛋白质景观.

Nicholas E Charron1,2,3,4, Klara Bonneau2, Aldo S Pasos-Trejo2

  • 1Department of Supercomputing, Zuse Institute Berlin, Berlin, Germany.

Nature chemistry
|July 18, 2025
PubMed
概括

研究人员使用深度学习开发了一种快速,通用的粗粒蛋白 (CG) 蛋白模型. 这种计算效率高的模型准确地预测了蛋白质结构和动态,克服了传统全原子模拟的局限性.

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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相关实验视频

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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科学领域:

  • 计算生物学 计算生物学
  • 蛋白质动力学 蛋白质动力学
  • 机器学习在生物化学中的应用

背景情况:

  • 全原子分子动力学模拟具有很高的预测能力,但计算成本昂贵.
  • 开发一个计算效率高的粗粒度 (CG) 模型,具有对蛋白质的普遍预测能力,仍然是一个重大挑战.

研究的目的:

  • 为蛋白质模拟创建一个通用的,计算效率高的粗粒度 (CG) 力场.
  • 为了实现与全原子模型可比的预测性能,但计算成本显著降低.

主要方法:

  • 结合了深度学习技术和大量数据集的全原子蛋白模拟.
  • 开发了一个由化学可转移性特征的自下而上的CG力场.
  • 在新型蛋白质序列上启用了推断分子动力学.

主要成果:

  • 开发的CG模型准确地预测了元稳定状态 (折叠,展开,中间结构).
  • 成功地模拟了内在无序蛋白质的波动.
  • 预测蛋白质突变物的相对折叠自由能量,效率高,比全原子方法快几倍.

结论:

  • 证明了用于蛋白质模拟的通用,机器学习的CG模型的可行性.
  • 突出了深度学习在加速分子动力学和蛋白质结构预测方面的潜力.