Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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...
12.8K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.2K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.8K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

An interpretable molecular descriptor for machine learning predictions in atmospheric science.

The Journal of chemical physics·2026
Same author

Synergistic Effects in Matrix-Embedded Alloy Nanoclusters: Advanced Type-I Photosensitizers for Theranostics.

ACS applied materials & interfaces·2026
Same author

TheMeCat: Dataset of Thermocatalytic Conversion of CO<sub>2</sub> to Methanol.

Scientific data·2026
Same author

Traffic-Emitted Amines Promote New Particle Formation at Roadsides.

ACS ES&T air·2025
Same author

Predicting intersystem crossing rate constants of alkoxy-radical pairs with structure-based descriptors and machine learning.

Physical chemistry chemical physics : PCCP·2025
Same author

SP-LCC - a dataset on the structure and properties of lignin-carbohydrate complexes from hardwood.

Scientific data·2025
Same journal

Anharmonic phonons via quantum thermal bath simulations.

The Journal of chemical physics·2026
Same journal

Quantum simulation of alignment dependent differential cross sections in co-propagating molecular beams at cold collision energies.

The Journal of chemical physics·2026
Same journal

Non-additive ion effects on the coil-globule equilibrium of a generic polymer in aqueous salt solutions.

The Journal of chemical physics·2026
Same journal

Insights into the unexpected small reduction of the temperature of maximum density of water by lithium chloride addition.

The Journal of chemical physics·2026
Same journal

Optical frequency comb double-resonance spectroscopy of the 9030-9175 cm-1 states of ethylene.

The Journal of chemical physics·2026
Same journal

Time reversal breaking of colloidal particles in cells.

The Journal of chemical physics·2026
查看所有相关文章

相关实验视频

Updated: Jul 1, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.0K

机器学习加速结构搜索对联体保护集群的搜索.

Lincan Fang1, Jarno Laakso1, Patrick Rinke1

  • 1Department of Applied Physics, Aalto University, 00076 AALTO, Espoo, Finland.

The Journal of chemical physics
|March 1, 2024
PubMed
概括
此摘要是机器生成的。

机器学习加速了在被联体保护的集群中寻找低能结构的搜索. 对于由氨酸配体保护的黄金集群,氨酸中的特定键影响结构和电子性质.

更多相关视频

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.6K
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.3K

相关实验视频

Last Updated: Jul 1, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.0K
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.6K
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.3K

科学领域:

  • 计算化学的计算化学
  • 材料科学 材料科学 材料科学
  • 机器学习 机器学习

背景情况:

  • 确定由连接体保护的集群的低能结构是计算密集的.
  • 庞大的结构空间对准确的结构预测构成了重大挑战.

研究的目的:

  • 开发和应用一种机器学习方法,以加快对联体保护集群的低能结构的搜索.
  • 调查由氨酸连接体保护的黄金集群的结构和电子特性.

主要方法:

  • 使用基于内核刚性回归的机器学习方法.
  • 采用Au25(Cys) 18集群作为方法验证的模型系统.

主要成果:

  • 成功加速了对低能耗结构的搜索.
  • 确定了氨酸中的特定键配置,作为低能结构的关键特征.
  • 证明了连接体层的配置会影响集群属性.

结论:

  • 机器学习方法对于预测联体保护集群的低能结构是有效的.
  • 囊配体中的键在黄金集群的稳定性和结构中起着至关重要的作用.
  • 连接物排列显著影响这些纳米材料的电子和结构特征.