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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
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
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
Protein Organization01:24

Protein Organization

6.5K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
6.5K
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

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相关实验视频

Updated: Jul 6, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

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

Published on: February 23, 2024

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基于结构的深度学习模型用于蛋白质 - 连接体结合亲缘关系预测.

Debby D Wang1, Wenhui Wu2,3, Ran Wang4,5,6

  • 1School of Science and Technology, Hong Kong Metropolitan University, 81 Chung Hau Sreet, Ho Man Tin, Hong Kong, China.

Journal of cheminformatics
|January 4, 2024
PubMed
概括
此摘要是机器生成的。

深度学习显示了预测蛋白质-体结合亲和力的前景,这是分子结构科学中的一个关键挑战. 本文审查了深度学习方法,为基于结构的药物发现提供了洞察力.

关键词:
结合性亲和力预测的预测深度学习是一种深度学习.可以解释性 解释性分子表示的分子表示.基于结构的药物发现.

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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科学领域:

  • 分子结构科学 分子结构科学
  • 计算化学是一种计算化学.
  • 生物物理学的生物物理.

背景情况:

  • 深度学习已经推进了分子结构科学,以AlphaFold系列为例.
  • 预测蛋白质 - 连接体结合亲和力是一个关键的挑战,需要复杂的计算方法.

研究的目的:

  • 审查主流基于结构的深度学习方法来预测蛋白质-配体结合亲和力.
  • 评估深度学习对这个复杂问题的准备程度.
  • 为比较不同深度学习模型提供统一的基础.

主要方法:

  • 专注于分子表示,学习架构和深度学习模型中的模型解释性.
  • 创建了现有的深度学习模型的分类.
  • 在统一的基础上评估代表性模型,以便进行有效的比较.

主要成果:

  • 识别和分类主流基于结构的深度学习方法.
  • 提供了对模型优点和缺点的比较分析.
  • 强调需要标准化的评估协议.

结论:

  • 深度学习具有很大的潜力,可以推进蛋白质-连接体结合亲和力预测.
  • 该综述为理解和比较该领域的深度学习模型提供了一个框架.
  • 结果可以使基于结构的药物发现和相关研究领域受益.