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相关概念视频

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...
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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
Ligand Binding Sites02:40

Ligand Binding Sites

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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...
12.8K
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

165
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...
165
Protein Networks02:26

Protein Networks

3.9K
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,...
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相关实验视频

Updated: Jun 26, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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DeepPPAPredMut:用于预测突变后蛋白质-蛋白质复合体中结合亲和力变化的深层组合方法.

Rahul Nikam1, Sherlyn Jemimah1,2, M Michael Gromiha1,3

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

Bioinformatics (Oxford, England)
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概括

我们开发了一个深度组合模型来预测突变如何改变蛋白质与蛋白质的结合亲和力. 该工具准确预测有约束力的变化,有助于疾病研究和药物发现.

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科学领域:

  • 计算生物学 计算生物学
  • 生物化学 生化学
  • 基因组学就是基因组学.

背景情况:

  • 蛋白质与蛋白质之间的相互作用对于细胞功能至关重要.
  • 突变破坏这些相互作用可以导致疾病.
  • 需要准确预测结合亲和力变化.

研究的目的:

  • 开发一种计算工具,用于预测蛋白质与蛋白质结合亲和力的突变诱导的变化.
  • 利用深度学习和结构特征来提高预测准确度.

主要方法:

  • 开发了一种集成蛋白序列,基于结构的特征和功能类的深度合并模型.
  • 使用实验亲和数据进行培训和验证.
  • 进行了严格的测试,包括Leave-One-Out Complex (LOOC) 交叉验证.

主要成果:

  • 在训练数据上取得了高性能 (相关性=0.97,MAE=0.35 kcal/mol).
  • 在测试数据上证明了强大的概括 (相关性=0.72,MAE=0.83 kcal/mol).
  • 对LOOC的交叉验证显示出强的性能 (相关性=0.83,MAE=0.51 kcal/mol).

结论:

  • 开发的深层组合模型准确地预测了对蛋白质结合亲和力的突变效应.
  • 该工具为了解疾病机制和指导治疗策略提供了宝贵的资源.
  • 该模型在验证集中的一致性能突显了其可靠性.