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相关概念视频

Molecular Models02:00

Molecular Models

38.4K
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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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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Assembly of Cytoskeletal Filaments01:18

Assembly of Cytoskeletal Filaments

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Cytoskeletal filaments are polymeric forms of smaller protein subunits. However, individual cytoskeletal filaments may easily disassemble or associate with other similar filaments to form rigid structures. Microfilaments, made of actin monomers, rely on actin-binding proteins to form bundles and create networks of individual actin filaments. Microtubules rely on microtubule-associated proteins (MAPs) to form sturdy cylindrical structures. However, the proteins involved in forming complex...
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Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

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Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
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相关实验视频

Updated: Jul 6, 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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从简单到复杂:使用cg2all从粗粒度模型重建全原子结构.

Yui Tik Pang1, Lixinhao Yang2, James C Gumbart3

  • 1School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Structure (London, England : 1993)
|January 5, 2024
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型,cg2all,从简化的粗粒度表示来准确预测全原子蛋白质结构. 蛋白质结构预测的这一突破提供了高效和准确的建模,即使输入数据最小.

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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
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A Protocol for Computer-Based Protein Structure and Function Prediction
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相关实验视频

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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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科学领域:

  • 结构生物学是结构生物学.
  • 计算生物学是一种计算生物学.
  • 生物物理学的生物物理.

背景情况:

  • 预测蛋白质结构对于理解生物功能至关重要.
  • 当前的方法往往需要详细的输入或是计算密集型.
  • 粗粒度 (CG) 模型简化了蛋白质表示,但往往牺牲了准确性.

研究的目的:

  • 推出cg2all,一种用于预测全原子蛋白质结构的新型深度学习模型.
  • 用CG表示来评估cg2all的效率和准确性.
  • 用高度简化的CG输入来证明模型的能力.

主要方法:

  • 开发了cg2all深度学习架构.
  • 使用粗粒 (CG) 蛋白质表示作为输入.
  • 与全原子结构对比预测的准确性.

主要成果:

  • 该cg2all模型有效地从CG数据中预测所有原子蛋白质结构.
  • 即使CG模型被减少到每个残留物的单个珠子时,也保持了高精度.
  • 对结构生物学中的各种应用有明显的潜力.

结论:

  • cg2all代表了计算蛋白质结构预测的重大进步.
  • 该模型提供了一种高效和准确的方法,可以从简化表示中获得所有原子的细节.
  • cg2all对加速结构生物学和药物发现研究具有有希望的意义.