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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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Drug Discovery: Overview01:26

Drug Discovery: Overview

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Protein Networks02:26

Protein Networks

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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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Drug-Receptor Bonds01:25

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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.
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Conserved Binding Sites01:49

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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.
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Drug-Receptor Interactions01:29

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Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
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相关实验视频

Updated: Jul 8, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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一种基于序列相互作用信息挖掘的深度学习方法,用于药物向 afinity 预测.

Mingjian Jiang1, Yunchang Shao1, Yuanyuan Zhang1

  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China.

PeerJ
|December 15, 2023
PubMed
概括
此摘要是机器生成的。

KC-DTA是一种新的深度学习方法,通过分析目标序列和分子图,准确地预测药物向亲和力 (DTA). 这种方法加快了in silico药物发现,减少了对昂贵的湿实验室实验的需求.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.药物目标亲和力预测的预测.图表神经网络的神经网络蛋白质的序列 蛋白质的序列

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相关实验视频

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

  • 计算生物学 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 药物发现

背景情况:

  • 准确的药物向亲和力 (DTA) 预测对于有效的in silico药物发现至关重要.
  • 实验性DTA确定是昂贵和耗时的,需要先进的计算方法.
  • 深度学习为DTA预测提供了一个强大的方法,因为它能够处理复杂的数据.

研究的目的:

  • 引入KC-DTA,一种基于序列的新型深度学习框架,用于预测药物向亲和力 (DTA).
  • 利用序列和图表表示来提高DTA预测的准确性.
  • 提供一种可访问和有效的工具,以加快在体中发现药物的速度.

主要方法:

  • 目标序列被转换成两个矩阵使用k-mer分析和卡特西亚产物,捕获残留相互作用和进化信息.
  • 分子化合物以图形表示,原子作为节点,键作为边缘.
  • 卷积神经网络 (CNN) 和图形神经网络 (GNN) 处理这些表示来提取用于DTA预测的特征.

主要成果:

  • KC-DTA方法在预测药物标亲和力方面表现出很高的表现.
  • 综合性比较证实了KC-DTA与最先进的方法相比的有效性.
  • 实验结果验证了KC-DTA作为DTA预测的重大进步.

结论:

  • KC-DTA是in silico药物发现的宝贵工具,为实验方法提供了计算效率高的替代方案.
  • 该方法有望加速药物开发管道.
  • 该研究提供了对数据和代码的开放访问,促进了进一步的研究和应用.