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相关概念视频

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
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,...
3.9K
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

36
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
36
Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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
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
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

2.5K
Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...
2.5K

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相关实验视频

Updated: Jun 12, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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基于深度学习的化合物-蛋白相互作用的一般预测模型.

Wei Ji1,2, Shengnan She1, Chunxue Qiao1

  • 1School of Pharmacy, Jiangsu University, Zhenjiang, China.

Frontiers in pharmacology
|September 19, 2024
PubMed
概括

一个新的深度学习模型准确地预测化合物-蛋白相互作用 (CPI),有助于药物发现. 这种计算工具增强了协同药物组合的识别,特别是来自传统中医 (TCM) 的药物组合,通过实验验证.

关键词:
化合物-蛋白质相互作用基于深度学习的预测模型.概括能力 概括能力的能力.传统的中医药是传统的中医药.没有偏见的大规模负面数据集.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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相关实验视频

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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A Protocol for Computer-Based Protein Structure and Function Prediction
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科学领域:

  • 计算生物学是一种计算生物学.
  • 药理学 药理学是指药理学的学科.
  • 生物信息学是一种生物信息学.

背景情况:

  • 化合物-蛋白相互作用 (CPI) 对药物发现和理解药物机制至关重要.
  • 现有的计算方法由于数据限制和多种化合物/目标而难以准确和概括.
  • 传统中医 (TCM) 提供了丰富的化合物来源,但识别它们的点和相互作用仍然具有挑战性.

研究的目的:

  • 利用深度学习开发一个强大的计算模型,用于准确的CPI预测.
  • 应用该模型来识别TCM化合物的标,并预测协同作用的药物组合.
  • 为了实验验证预测的协同作用药物组合的潜在治疗应用.

主要方法:

  • 开发了一个深度学习 (DL) 模型,集成了一个大规模的生物活性数据集,用于CPI预测.
  • 应用DL模型来预测TCM草药对*Astragalus membranaceus*和*Hedyotis diffusa*中的活性化合物的标.
  • 使用预测目标通过DeepMDS模型识别协同作用的多化合物组合,并通过*体外*试验验验证.

主要成果:

  • DL模型实现了高性能,AUROC为0.98,AUPR为0.98,精度为93.31%.
  • 成功预测了从研究的TCM草药对中扩展的化合物标数据集.
  • *体外*实验证实了对乳腺癌细胞 (MDA-MB-231) 预测的协同作用组合的有效性.

结论:

  • 开发的CPI预测模型展示了卓越的准确性和概括能力.
  • 该模型促进了新型CPI和协同药物组合的发现,特别是来自TCM.
  • 这种方法显示出加速药物发现和阐明TCM生物活性化合物的巨大潜力.