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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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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...
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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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Drug-Receptor Bonds01:25

Drug-Receptor Bonds

3.3K
Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
In...
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Protein Networks02:26

Protein Networks

4.1K
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: Sep 11, 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

Published on: January 26, 2024

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多尺度的动机意识关系图形结构用于药物标绑定亲和力预测.

Cheng Cheng, Xiaohong Zhang, Mianyang Yu

    IEEE transactions on computational biology and bioinformatics
    |August 14, 2025
    PubMed
    概括

    这项研究引入了一种新型的多尺度动机意识关系图 (MMRG) 来增强药物标结合亲和力预测. MMRG捕获关键的药物结构信息,优于传统的SMILES表示来改善药物发现.

    科学领域:

    • 计算化学的计算化学
    • 药物发现 药物发现 药物发现
    • 生物信息学是一种生物信息学.

    背景情况:

    • 药物标结合亲和力 (DTA) 的预测对于有效的药物发现至关重要.
    • 现有的方法通常依赖于1D SMILES,忽视了重要的分子结构细节.
    • 整合结构信息是准确预测药物特性的关键.

    研究的目的:

    • 开发一种新的描述符,即多尺度动机感知关系图 (MMRG),用于改进DTA预测.
    • 利用动机级结构和拓信息来增强药物表现.
    • 通过结合先进的基于图表的功能,超越现有的DTA预测方法.

    主要方法:

    • 为药物提出了一种新的MMRG构建方法.
    • 实现了多层次的模式意识学习,从各种模式大小中提取结构信息.
    • 利用关系图形构造来捕获化学键的拓信息.
    • 使用图形卷积网络 (GCN) 来学习MMRG的潜在特征.

    主要成果:

    • 提出的MMRG方法显著提高了戴维斯和KIBA数据集上的DTA预测准确度.
    • 与最先进的方法相比,在戴维斯数据集上获得了15.61%的平均性能改善,在KIBA数据集上获得了8.50%的平均性能改善.

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    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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    Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
    08:49

    Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

    Published on: June 20, 2025

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    相关实验视频

    Last Updated: Sep 11, 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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    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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    Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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    Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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  • 证明了MMRG在捕获基本结构和拓药物特征方面的有效性.
  • 结论:

    • MMRG描述符提供了比SMILES更全面的药物分子表示.
    • 拟议的方法在药物向亲和度预测准确度方面取得了重大进展.
    • 这种方法有望通过实现更精确的预测来加速药物发现管道.