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相关概念视频

Molecular Models02:00

Molecular Models

37.8K
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.
37.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

26
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
26
Ligand Binding Sites02:40

Ligand Binding Sites

12.7K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.7K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

39
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
39
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

48.0K
Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
48.0K
Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K

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相关实验视频

Updated: Jun 2, 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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对于分子关联的基于网格的模型.

Hana Zupan1, Bettina G Keller1

  • 1Department of Biology, Chemistry and Pharmacy, Freie Universität Berlin, Arnimallee 22, 14195 Berlin, Germany.

Journal of chemical theory and computation
|January 13, 2025
PubMed
概括

本研究介绍了一种基于网格的分子关联建模方法,为传统的马尔科夫模型提供了一个计算效率高的替代方案. 该方法准确地识别了分子结合机制和元稳定状态.

科学领域:

  • 计算化学是一种计算化学.
  • 分子动力学分子动力学
  • 化学动力学 化学动力学

背景情况:

  • 传统的分子关联马尔科夫模型依赖于广泛的抽样.
  • 现有的方法可能是计算密集的,需要大量的分子动力学模拟.
  • 对分子关联的准确建模对于理解生化过程至关重要.

研究的目的:

  • 开发一种基于网格的方法来建模分子关联过程.
  • 为基于采样的马尔科夫模型提供一个计算高效的替代方案.
  • 为了能够准确地识别元稳定状态和结合机制.

主要方法:

  • 将相对转换和方向的六维空间分离到网格单元中.
  • 使用平方根近似来导出分析过渡速率常数的福克-普朗克运算符的分离.
  • 基于几何网格属性和网格细胞中心的分子能量计算过渡速率.

主要成果:

  • 基于网格的方法为过渡速率常数提供了分析表达式.
  • 这种方法通过最小化能源评估来降低计算成本.
  • 导出速率矩阵提供了对元稳定状态和关联动力学的见解,可与马尔科夫状态模型相比较.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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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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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

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结论:

  • 基于网格的方法是研究分子关联的计算效率高,系统的工具.
  • 该方法准确地识别了超稳定状态和结合机制.
  • 该方法灵活,适用于各种分子系统和能量功能,有进一步改进的潜力.