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

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
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
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
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.9K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
12.9K
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

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

Advances in polarised luminescence approaches to understanding interactions between biomolecules.

Biochemical Society transactions·2026
Same author

A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.

Drug metabolism and disposition: the biological fate of chemicals·2026
Same author

Emerging in Diabetic Cardiomyopathy: Molecular Pathways and Targets for Therapeutic Intervention.

Drug development research·2025
Same author

Epigenetics in forest trees- key driver to phenotypic plasticity and adaptation under stress.

Functional & integrative genomics·2025
Same author

A Novel Combination of Exogenous Klotho Combined With Telmisartan Ameliorated Diabetic Cardiomyopathy via an Antifibrotic Mechanism.

Cell biology international·2025
Same author

Histone demethylase inhibitors: developmental insights and current status.

Future medicinal chemistry·2025

相关实验视频

Updated: Jul 5, 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

使用机器学习预测模型进行生物活性预测和虚拟查.

Noor Fatima Siddiqui1, Pinky Vishwakarma1, Shikha Thakur1

  • 1Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India.

Journal of biomolecular structure & dynamics
|January 13, 2024
PubMed
概括
此摘要是机器生成的。

一个新的机器学习模型预测了酶抑制剂,解决了药物发现中的化学偏差. 随机森林模型被证明是最准确的,识别了潜在的DPP-4抑制剂.

关键词:
DPP-4 抑制剂的使用机器学习预测模型mMGBSASA 的意思是分子对接的分子对接.分子动力学模拟模拟

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

1.9K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K

相关实验视频

Last Updated: Jul 5, 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

1.9K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K

科学领域:

  • 计算化学是一种计算化学.
  • 药品化学 药品化学 是一个
  • 机器学习是机器学习.

背景情况:

  • 对酶抑制剂的预测建模至关重要,但受到化学偏差和缺乏可重复性的限制.
  • 现有的模型往往无法解释化学多样性,阻碍了药物发现工作.

研究的目的:

  • 开发一种新的机器学习模型,用于预测化学偏差的酶抑制剂.
  • 为了评估模型的有效性,使用Dipeptidyl peptidase 4 (DPP-4) 抑制剂,并验证其性能.

主要方法:

  • 使用随机森林算法开发机器学习模型.
  • 使用各种训练/测试数据与随机分割对模型性能进行比较.
  • 在药物银行数据库中对DPP-4抑制剂进行in-silico选.
  • 通过分子对接和分子动力学模拟进行验证.

主要成果:

  • 随机森林算法在经过测试的机器学习算法中显示了最高的准确性.
  • 基于Murcko支架的开发模型有效地解决了化学偏差的担忧.
  • 在体查中,从药物银行数据库中确定了两种已知的DPP-4抑制剂.
  • 分子对接和动力学模拟证实了该模型的预测可信度.

结论:

  • 结合Murcko支架的机器学习模型可以克服酶抑制剂预测中的化学偏差.
  • 开发的模型显示了对高效的药物发现和潜在的临床转化有希望.
  • 该方法提供了一种可靠的方法来识别新药候选药物.