Jove
Visualize
联系我们

相关概念视频

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
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
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
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-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
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

50.7K
Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
50.7K

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

OrgNet: orientation-gnostic protein stability assessment using convolutional neural networks.

Bioinformatics (Oxford, England)·2025
Same author

Computational methods for binding site prediction on macromolecules.

Quarterly reviews of biophysics·2025
Same author

Approaching Optimal pH Enzyme Prediction with Large Language Models.

ACS synthetic biology·2024
Same author

Biphenyl scaffold for the design of NMDA-receptor negative modulators: molecular modeling, synthesis, and biological activity.

RSC medicinal chemistry·2022
Same author

Structure-based deep learning for binding site detection in nucleic acid macromolecules.

NAR genomics and bioinformatics·2021
Same author

Protein-Peptide Binding Site Detection Using 3D Convolutional Neural Networks.

Journal of chemical information and modeling·2021
Same journal

PFASGroups: An Open-Source Framework for Automated Identification, Structural Classification, and Prioritization of Per- and Polyfluoroalkyl Substances.

Journal of chemical information and modeling·2026
Same journal

DeepKbhb: Context-Aware Prediction of Human Lysine β-Hydroxybutyrylation Sites.

Journal of chemical information and modeling·2026
Same journal

HyperDC: A Non-Uniform Hypergraph Framework for Dual- and Higher-Order Drug Combination Recommendation Across Diverse Complex Diseases.

Journal of chemical information and modeling·2026
Same journal

Correction to "AstraMEV (AI-Guided Structural Assembly of Multi-Epitope Vaccines) Against Infectious Bronchitis Virus".

Journal of chemical information and modeling·2026
Same journal

MolPy: A Large Language Model-Friendly Toolkit for Reactive Topology Editing in Polymer Simulations.

Journal of chemical information and modeling·2026
Same journal

Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.

Journal of chemical information and modeling·2026
查看所有相关文章
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

相关实验视频

Updated: Jul 2, 2025

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

graphLambda:用于绑定亲和力预测的融合图神经网络

Ghaith Mqawass1,2, Petr Popov3,4

  • 1Faculty of Computer Science, University of Vienna, Vienna A-1090, Austria.

Journal of chemical information and modeling
|February 17, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了graphLambda,这是一个新的深度学习模型,用于预测蛋白质 - 配体结合亲和力. 图形神经网络的这种进步通过提高识别潜在药物候选者的准确性来增强计算机辅助药物发现 (CADD).

更多相关视频

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
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

相关实验视频

Last Updated: Jul 2, 2025

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
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
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

科学领域:

  • 计算化学是一种计算化学.
  • 化学信息学 化学信息学
  • 生物信息学是一种生物信息学.

背景情况:

  • 准确预测蛋白质 - 配体结合亲和力对于药物发现至关重要.
  • 深度学习得分函数显示为预测绑定常数具有前途.
  • 图形神经网络 (GNN) 为分子建模提供了先进的功能.

研究的目的:

  • 介绍graphLambda,一种基于GNN的新型模型,用于增强蛋白质 - 配体结合亲和力预测.
  • 提高计算机辅助药物发现 (CADD) 的结合亲和力预测的准确性和稳定性.

主要方法:

  • 在GNN架构中利用了图形卷积,注意力和异态块.
  • 开发了一种新的深度学习模型,命名为 graphLambda.
  • 在已建立的基准上评估模型性能,如CASF16和CSAR HiQ NRC.

主要成果:

  • graphLambda在CASF16和CSAR HiQ NRC基准上表现出卓越的预测性能.
  • 该模型在各种列车验证集分区策略中显示出稳定性.
  • 通过专门的图形神经网络块实现了增强的预测能力.

结论:

  • graphLambda代表了基于GNN的结合亲和力预测的重大进步.
  • 该模型具有更有效的CADD方法的潜力.
  • 突出了GNN在计算药物发现中的日益重要.