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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

4.0K
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,...
4.0K
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

184
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...
184
Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

483
Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
483
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
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

45
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...
45

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相关实验视频

Updated: Jul 5, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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使用特征选择和分类技术的药物蛋白相互作用预测模型.

T Idhaya1, A Suruliandi1, S P Raja2

  • 1Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India.

Current drug metabolism
|January 25, 2024
PubMed
概括
此摘要是机器生成的。

这项研究通过优化机器学习模型来增强药物蛋白相互作用 (DPI) 预测. 它确定了最好的平衡,特征选择和分类技术,用于在药物发现中准确的DPI识别.

关键词:
药物发现 药物发现化学基因组学 化学基因组学分类技术. 分类技术.药物蛋白相互作用 药物蛋白相互作用功能选择 功能选择机器学习是机器学习.

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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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相关实验视频

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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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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:

  • 计算化学和化学信息学
  • 生物信息学和计算生物学
  • 机器学习在药物发现中的作用

背景情况:

  • 药物蛋白相互作用 (DPI) 识别对药物发现至关重要,但高维数据存在挑战.
  • 现有的计算方法,如基于对接和基于带的方法有局限性.
  • 基于化学遗传学的机器学习方法通过整合药物和蛋白质特征提供了一个有希望的解决方案.

研究的目的:

  • 提高药物蛋白相互作用 (DPI) 预测的准确性和效率.
  • 为应对DPI数据集中高维度和数据不平衡所带来的挑战.
  • 为DPI预测确定最佳的机器学习策略.

主要方法:

  • 利用了KEGG的蛋白质数据和DrugBank的药物数据.
  • 应用和评估了各种数据平衡技术:随机采样 (ROS),SMOTE和自适应SMOTE.
  • 评估的特征选择方法包括相关性,信息获取 (IG),奇方位 (CS) 和浮纹.
  • 使用和比较分类算法:支持矢量机器 (SVM),随机森林 (RF),Adaboost和后勤回归 (LR).

主要成果:

  • 评估了不同平衡技术的有效性,以处理不平衡的药物蛋白对 (DPP).
  • 对比多种特征选择方法,以确定最有信息的药物和蛋白质特征.
  • 确定了平衡,特征选择和分类方法的最佳组合,以准确预测DPI.

结论:

  • 该研究成功地确定了用于DPI预测的机器学习技术最有效的组合.
  • 这种优化的方法提高了计算药物蛋白相互作用研究的可靠性和效率.
  • 这些发现有助于在药物发现和开发领域做出更准确的预测.