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相关概念视频

Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

238
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...
238
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
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 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
Protein Networks02:26

Protein Networks

4.0K
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
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.0K
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:
13.0K

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相关实验视频

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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基于深度学习的方法,用于预测和分类蛋白质-蛋白质复合体的结合亲和力.

Rahul Nikam1, Kumar Yugandhar2, M Michael Gromiha3

  • 1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India.

Biochimica et biophysica acta. Proteins and proteomics
|August 11, 2023
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概括

这项研究引入了一个深度学习模型来预测蛋白质与蛋白质相互作用的结合亲和力 (ΔG). 该模型准确地估计了结合强度,有助于理解分子识别.

关键词:
结合性亲和力预测预测深度学习是一种深度学习.吉布斯的自由能量是自由的.蛋白质与蛋白质的相互作用

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科学领域:

  • 计算生物学 计算生物学
  • 生物物理学的生物物理.
  • 生物信息学是一种生物信息学.

背景情况:

  • 蛋白与蛋白相互作用 (PPI) 是细胞功能的基础.
  • 准确估计PPI结合亲和力对于理解分子识别至关重要.

研究的目的:

  • 开发一种深度学习模型,用于预测蛋白质-蛋白质复合体结合亲和力 (ΔG).
  • 为研究PPI提供一个高效的计算工具.

主要方法:

  • 编制了903个具有实验性结合亲和性的蛋白质-蛋白质复合物的数据集.
  • 从序列中提取了8-20个特征,并预测了每个功能类的3D结构.
  • 利用特征选择和深度学习方法进行亲和力预测.

主要成果:

  • 平均绝对误差为1.05 kcal/mol,预测和实验ΔG之间的相关性为0.79.
  • 在使用 10 倍交叉验证区分高和低亲和力 PPI 中,证明了 87% 的准确性和 0.86 F1 评分.
  • 开发了一个用于预测PPI结合亲和力的Web服务器.

结论:

  • 深度学习方法提供了一种有效的方法来预测PPI结合亲和力.
  • 该模型提供了关于蛋白质与蛋白质相互作用中的分子识别机制的见解.
  • 开发的Web服务器是研究PPI的研究人员的宝贵资源.