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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

4.4K
4.4K
Protein Networks02:26

Protein Networks

4.5K
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,...
4.5K
Conserved Binding Sites01:49

Conserved Binding Sites

5.0K
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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Ligand Binding Sites02:40

Ligand Binding Sites

14.9K
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...
14.9K
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

616
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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相关实验视频

Updated: Jan 16, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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基于序列的蛋白-蛋白相互作用预测及其在药物发现中的应用.

François Charih1,2,3, James R Green1,3, Kyle K Biggar2,3

  • 1Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada.

Cells
|September 26, 2025
PubMed
概括

本综述强调了基于序列的蛋白质-蛋白质相互作用 (PPI) 预测方法. 这些计算方法对于识别疾病点和推动药物发现至关重要.

关键词:
生物学的生物学.发现药物的发现.药物目标识别 药物目标识别蛋白质语言模型的模型蛋白蛋白相互作用预测和预测基于序列的模型.

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相关实验视频

Last Updated: Jan 16, 2026

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 药物发现 药物发现

背景情况:

  • 异常的蛋白质与蛋白质相互作用 (PPI) 涉及到许多人类疾病.
  • 针对这些有害相互作用是一个有前途的治疗策略.
  • 计算方法,特别是深度学习,正在推动PPI预测.

研究的目的:

  • 审查最新的基于序列的PPI预测方法.
  • 探索PPI预测对目标识别和药物发现的影响.
  • 在PPI预测中强调严格的模型评估.

主要方法:

  • 对PPI预测的数据源和策划技术的概述.
  • 对传统基于相似性和基于深度学习的PPI预测指标的调查.
  • 强调PPI预测的深度学习中的变压器架构.

主要成果:

  • 基于序列的PPI预测为基于结构的方法提供了一个可行的替代方案.
  • 提供了蛋白质组学中PPI预测,目标识别和治疗设计的例子.
  • 审查强调了数据质量和模型评估的重要性.

结论:

  • 基于序列的PPI预测是生物医学研究中广泛适用的工具.
  • 这些方法对确定治疗点和设计新药作出了重大贡献.
  • 计算方法的进步正在彻底改变疾病治疗策略.