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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
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...
4.1K
Ligand Binding Sites02:40

Ligand Binding Sites

12.6K
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...
12.6K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.7K
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:
12.7K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.4K
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...
12.4K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.7K
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...
4.7K
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

46.3K
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,...
46.3K

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Updated: May 15, 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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一个折叠-对接-亲和关系框架,用于蛋白质-配体结合亲和关系预测.

Ming-Hsiu Wu1, Ziqian Xie2, Degui Zhi3

  • 1McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA. Ming.Hsiu.Wu@uth.tmc.edu.

Communications chemistry
|April 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的框架,通过整合人工智能驱动的蛋白质折叠和构造预测来预测蛋白质-连接体结合性亲和力. 折叠对接亲和力 (FDA) 方法显示了与现有方法相比较的性能,为基于结构的亲和力预测铺平了道路.

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科学领域:

  • 计算化学的计算化学
  • 结构生物学 结构生物学
  • 药物发现 药物发现 药物发现

背景情况:

  • 准确的蛋白质 - 配体结合亲和力预测对于高效的药物发现至关重要.
  • 当前的无对接方法往往忽略了当3D结合结构不可用时的明确原子级相互作用.
  • 基于人工智能的蛋白质结构预测的进步提供了新的机会.

研究的目的:

  • 开发和评估一个整合蛋白质折叠,结合形状确定和亲和力预测的框架.
  • 评估利用预测的3D蛋白质-连接体结合结构用于亲和力预测的实用性.
  • 探索AI在提高结合亲和力预测准确度方面的潜力.

主要方法:

  • 开发了折叠对接亲和关系 (FDA) 框架.
  • 美国食品和药品管理局利用深度学习人工智能进行蛋白质折叠和结合形状预测.
  • 结合亲和性直接从生成的3D蛋白质-连接体结合结构中预测出来.

主要成果:

  • 美国食品和药物管理局的框架表明,性能与最先进的无对接方法相美.
  • 该研究成功地将预测的蛋白质结构集成到结合亲和力预测管道中.
  • 实验结果验证了拟议方法的可行性.

结论:

  • 美国食品和药物管理局的框架提供了一种可行的方法,用于使用预测结构来预测约束亲和力.
  • 整合预测的结合形状为提高准确性提供了一个有希望的途径.
  • 这项工作作为未来基于结构的亲和力预测方法的基础.