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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

12.6K
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...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Protein Networks02:26

Protein Networks

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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,...
4.0K
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
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
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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深度局部分析解构蛋白质-蛋白质接口,并准确估计突变时的结合亲和力变化.

Yasser Mohseni Behbahani1, Elodie Laine1, Alessandra Carbone1

  • 1Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Sorbonne Université, CNRS, IBPS, Paris 75005, France.

Bioinformatics (Oxford, England)
|June 30, 2023
PubMed
概括
此摘要是机器生成的。

深度局部分析 (DLA) 是一种新的深度学习方法,可以预测突变如何影响蛋白质结合亲和力. DLA实现了高精度和概括性,在复杂的蛋白质结构上表现优于现有的方法.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 结构生物信息学 结构生物信息学
  • 结构生物学中的机器学习

背景情况:

  • 蛋白质结构预测的进步使大规模的互动原子重建成为可能.
  • 对蛋白质-蛋白质相互作用的序列变化效应的准确建模至关重要.

研究的目的:

  • 引入深度局部分析 (DLA),这是一个新的深度学习框架,用于预测突变对蛋白质结合亲和力的影响.
  • 评估DLA在未见的蛋白质复合体上的性能和概括能力.

主要方法:

  • DLA将蛋白质接口分解成以残留为中心的3D立方体.
  • 它使用3D卷积来识别这些立方体内的模式.
  • 预测是使用野生类型和突变残留立方体来估计结合亲和力变化.

主要成果:

  • DLA准确地估计了结合亲和力变化,在大约400个突变中达到0.735的皮尔森相关系数.
  • 与最先进的方法相比,该框架在盲目数据集上展示了优越的概括性.
  • 结合进化约束可以提高预测的准确性.

结论:

  • DLA提供了一种有效和准确的方法来预测对蛋白质结合亲和力的突变效应.
  • DLA框架具有多功能性,适用于各种结构生物信息学任务,包括残留物识别和复杂功能预测.