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

12.9K
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...
12.9K
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
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.5K
VSEPR Theory for Determination of Electron Pair Geometries
34.5K
Molecular Models02:00

Molecular Models

38.6K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
38.6K
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

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

A Centralized AI Lakehouse Framework for Brain Tumor MRI Classification and Segmentation, University KPI Forecasting, and Water Potability Prediction.

Sensors (Basel, Switzerland)·2026
Same author

Smart optical biosensor for edible oil detection with machine learning integration.

Analytical biochemistry·2026
Same author

A novel electrochemical exfoliation route to tailor the graphene bandgap through silicon incorporation: semi-metallic to semiconducting transition.

Nanoscale advances·2026
Same author

Seed priming-induced enhancement in seed germination, Seedling vigor, and productivity of foxtail millet (Setaria italica L.) in winter and summer seasons under Bangladesh conditions.

PloS one·2026
Same author

Direct Synthesis of Exfoliated Carbon Nitride Mediated by Sodiated Cellulose Nanocrystals for Photocatalytic and Adsorbent Applications.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Machine learning-enhanced novel design and performance optimization of M<sub>3</sub>SbI<sub>3</sub> (M = Ba and Ca) based dual absorber perovskite solar cells.

RSC advances·2026

相关实验视频

Updated: Jul 20, 2025

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

具有扩展原子类型的几何图形学习功能,用于蛋白质-连接体结合亲缘关系预测.

Md Masud Rana1, Duc Duy Nguyen1

  • 1Department of Mathematics, University of Kentucky, Lexington, 40506, KY, USA.

Computers in biology and medicine
|July 29, 2023
PubMed
概括

这项研究引入了基于图形的新型机器学习模型SYBYL和ECIF,用于预测蛋白质-连接体结合亲和力. 在药物设计基准中,SYBYL模型显著优于现有方法.

科学领域:

  • 计算化学和化学信息学
  • 机器学习在药物发现中的作用
  • 分子建模和模拟分子模型

背景情况:

  • 准确预测蛋白质 - 配体结合亲和力对于高效的药物设计至关重要.
  • 机器学习 (ML) 方法越来越多地被使用,因为它们的准确性和数据的可用性越来越大.
  • 图形理论为建模分子相互作用提供了一个自然框架.

研究的目的:

  • 增强基于图形的机器学习模型,用于蛋白质 - 配体相互作用研究.
  • 将广泛的原子类型,特别是SYBYL和扩展连接交互功能 (ECIF) 集成到多尺度加权彩色图 (MWCG) 中.
  • 开发和验证结合亲缘关系的新预测模型.

主要方法:

  • 将SYBYL和ECIF原子类型集成到多尺度加权彩色图 (MWCG).
  • 应用梯度增强决策树 (GBDT) 机器学习算法.
  • 开发了两个模型:sybyl GGL-Score和ecif GGL-Score.
  • 使用CASF-2007,CASF-2013和CASF-2016基准数据集进行验证.

主要成果:

  • 无论是sybyl GGL-Score还是ecif GGL-Score都在基准数据集上取得了最先进的结果.
关键词:
原子类型的相互作用.学习几何图形的几何图形学习机器学习 机器学习蛋白质 - 连接物结合亲和力.有权重的彩色子图.

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: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.4K

相关实验视频

Last Updated: Jul 20, 2025

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
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.4K
  • 与其他最先进的方法相比,sybyl GGL-Score 模型在所有基准中都表现出优异的性能.
  • 性能最好的SYBYL原子型模型进一步在独立的测试集上得到了验证.
  • 结论:

    • 拟议的基于图形的机器学习方法显著改善了蛋白质-连接体结合亲和力的预测.
    • SYBYL原子型模型在药物设计的结合亲和力预测准确度方面取得了重大进展.
    • 这些模型为加速药物发现过程提供了强大的工具.