Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

165
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...
165
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

5.1K
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.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
5.1K
Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

429
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,...
429
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
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

6.2K
Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
6.2K
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

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

All-optical doubly resonant cavities for energy-efficient ReLU function in nanophotonic deep learning.

PloS one·2026
Same author

Enhancing protein structure prediction: evaluating the role of amino acid physicochemical features in homology search.

Briefings in bioinformatics·2026
Same author

DeepEPI: CNN-transformer-based model for extracting TF interactions through predicting enhancer-promoter interactions.

Bioinformatics advances·2025
Same author

InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks.

Heliyon·2025
Same author

Free-space optical spiking neural network.

PloS one·2025
Same author

scVAG: Unified single-cell clustering via variational-autoencoder integration with Graph Attention Autoencoder.

Heliyon·2024

相关实验视频

Updated: Jun 26, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.5K

DCGAN-DTA:预测药物标结合亲和力与深层卷积生成对抗网络.

Mahmood Kalemati1, Mojtaba Zamani Emani1, Somayyeh Koohi2

  • 1Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

BMC genomics
|May 9, 2024
PubMed
概括

这项研究引入了一个深度卷积生成对抗网络 (DCGAN) 来预测药物标结合亲和力,优于现有方法. 这种方法加速了药物发现和重新利用,为计算药物设计提供了有价值的工具.

关键词:
对抗性控制实验的实验.这是一个BLOSUM编码.深度卷积的生成对抗性网络.药物标结合亲和力 药物标结合亲和力草模型的模型

更多相关视频

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

1.8K
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

5.0K

相关实验视频

Last Updated: Jun 26, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.5K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

1.8K
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

5.0K

科学领域:

  • 计算化学是一种计算化学.
  • 药理学 药理学是指药理学的学科.
  • 机器学习是机器学习.

背景情况:

  • 计算方法越来越多地用于预测药物标结合亲和力,加速药物发现.
  • 现有的机器学习方法面临着诸如有限数据,手动功能工程和不足的验证等挑战.
  • 用于结合亲和力预测的实验方法昂贵且耗时.

研究的目的:

  • 开发一种新的深度卷积生成对抗网络 (DCGAN),用于准确的药物标结合亲和力预测.
  • 为了解决当前计算和实验药物发现方法的局限性.
  • 为药物发现提供一个强大的和验证的计算工具.

主要方法:

  • 利用深度卷积生成对抗网络 (DCGANs) 进行绑定亲和力预测.
  • 使用吸管模型进行了验证和对抗性控制实验.
  • 评估了使用热启动和冷启动设置对BindingDB和PDBBind数据集的方法.

主要成果:

  • 拟议的DCGAN方法与基线和最先进的方法相比,显示出优异的预测性能.
  • 在热启动条件下,在三个性能指标中表现优于其他替代品.
  • 显示了增强的预测准确性,特别是在对应指数中,用于基于生理化学的冷启动设置.

结论:

  • 基于DCGAN的方法为药物标结合亲和力预测提供了实用价值和卓越的性能.
  • 这种方法可以加速药物重新定位,新药发现和疾病治疗.
  • 源代码和Web服务器可用于更广泛的应用和验证.