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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.9K
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...
12.9K
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
Metal-Ligand Bonds02:51

Metal-Ligand Bonds

20.8K
The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
20.8K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.9K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
12.9K
Complexometric Titration: Ligands00:43

Complexometric Titration: Ligands

961
Different monodentate and polydentate ligands are used as complexing agents in complexometric titration reactions. The formation of complexes by mono- and bidentate ligands involves two or more intermediate steps, limiting their use as complexing agents. In comparison, polydentate ligands can form complexes with metal ions in a single-step process, facilitating sharper end points. This means polydentate ligands, such as amino carboxylic acid derivatives, are most commonly employed in...
961

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相关实验视频

Updated: Jul 9, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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使用机器学习对血性联体的分类.

Ilia Kevlishvili1, Chenru Duan1,2, Heather J Kulik1,2

  • 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

The journal of physical chemistry letters
|December 5, 2023
PubMed
概括

识别半叶状联结体是具有挑战性的. 机器学习模型通过分析协调球来准确地预测连接体中的 hemilability,从而改进了催化应用.

科学领域:

  • 协调化学 协调化学
  • 计算化学的计算化学
  • 材料科学 材料科学 材料科学

背景情况:

  • 血结合体提供了一种调整催化剂稳定性和反应性的方法.
  • 因其复杂的协调行为而难以识别半状联结体.

研究的目的:

  • 开发一种可靠的方法来识别半状连接体.
  • 为了利用机器学习来预测连接体的合性.

主要方法:

  • 利用剑桥结构数据库来识别具有不同密度的连接体.
  • 实施了一种半监督学习方法,使用标签扩散算法.
  • 开发了机器学习分类模型,以预测混杂性.

主要成果:

  • 仅仅基于协调原子身份的启发式是不够的识别 hemilabile 连接体.
  • 机器学习模型准确地预测了双,三和四联体的血性.
  • 功能重要性分析揭示了第二,第三和第四个协调领域的意义.

结论:

  • 机器学习提供了一种强有力的方法来识别半叶状连接体.
  • 了解协调球相互作用对于预测连接体 hemilability 是至关重要的.

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  • 这种方法可以推进高效催化剂的设计.