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

Conserved Binding Sites01:49

Conserved Binding Sites

5.0K
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...
5.0K
Conserved Binding Sites01:49

Conserved Binding Sites

1.9K
1.9K
Ligand Binding Sites02:40

Ligand Binding Sites

14.9K
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...
14.9K
Ligand Binding Sites02:40

Ligand Binding Sites

8.6K
8.6K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.3K
VSEPR Theory for Determination of Electron Pair Geometries
45.3K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K

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 novel method for drug-target affinity prediction by integrating predicted evolutionary information and multi-scale protein graphs.

BMC biology·2026
Same author

DPSM-Synergy: A Dual-Path Feature Extraction and Synergy Matrix Enhancement Method for Anti-Cancer Drug Synergy Prediction.

Journal of chemical information and modeling·2026
Same author

SeqMG-RPI: A Sequence-Based Framework Integrating Multi-Scale RNA Features and Protein Graphs for RNA-Protein Interaction Prediction.

Journal of chemical information and modeling·2025
Same author

Intelligent large-scale flue-cured tobacco grading based on deep densely convolutional network.

Scientific reports·2023
Same author

Enhancing Protein Function Prediction Performance by Utilizing AlphaFold-Predicted Protein Structures.

Journal of chemical information and modeling·2022
Same author

[EZH2 expression in human prostate cancer and its clinicopathologic significance].

Zhonghua nan ke xue = National journal of andrology·2010
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

Journal of chemical information and modeling·2026
Same journal

Derisking Affinity Optimization for Macrocycles and Cyclic Peptides: High-Precision Free Energy Simulations across Five Diverse Targets.

Journal of chemical information and modeling·2026
Same journal

An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.

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

相关实验视频

Updated: Jan 17, 2026

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

69.7K

ProtGeoNet-Pocket:一种结合性站点预测方法,集成序列,几何和图形结构.

Mingjian Jiang1, Zhi Zhang1, Teng Ma1

  • 1School of Information and Control Engineering, Qingdao University of Technology, No. 777 Jialingjiang East Road, Huangdao District, Qingdao, Shandong 266520, China.

Journal of chemical information and modeling
|September 17, 2025
PubMed
概括
此摘要是机器生成的。

通过整合序列,几何和图形数据,ProtGeoNet-Pocket准确地预测了蛋白质结合位置. 这种多式模式框架实现了高精度,在蛋白质结构分析中表现优于现有的方法.

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

2.5K
Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

16.0K

相关实验视频

Last Updated: Jan 17, 2026

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

69.7K
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

2.5K
Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

16.0K

科学领域:

  • 计算生物学 计算生物学
  • 结构生物信息学 结构生物信息学
  • 机器学习 机器学习

背景情况:

  • 蛋白质结合部位的识别对于理解生物功能和药物发现至关重要.
  • 现有的方法经常与复杂的3D结构和各种形状的蛋白质结合口袋作斗争.
  • 整合多模式数据 (序列,几何,图形) 为提高预测准确性提供了一个有希望的途径.

研究的目的:

  • 开发一个创新的多式预测框架,ProtGeoNet-Pocket,用于增强蛋白质结合位点识别.
  • 利用多个规模的结构信息来解决蛋白质结构复杂性和结合口袋形状多样性.
  • 通过有效的功能集成和先进的网络架构实现高精度的绑定站点预测.

主要方法:

  • 利用PointNet模块从残留坐标中提取几何特征,并通过注意力机制进行增强.
  • 融合的几何特征与编码的序列信息和图形边缘特征.
  • 采用了带有传递信息机制的图形同态网络 (GIN) 来捕获拓关系.

主要成果:

  • 在scPDB训练组中获得72.87%的F1分数.
  • 在五个独立的基准数据集 (COACH420,HOLO4K,SC6K,PDBbind,ApoHolo) 中表现出强大的预测性能.
  • 可视化证实了预测和实际结合地点之间的高空间重叠,表明性能优越.

结论:

  • ProtGeoNet-Pocket有效地整合了多式联运数据,以准确地预测蛋白质结合地点.
  • 与现有方法相比,该框架显示出更高的性能.
  • 这种方法在推动药物发现和蛋白质功能研究方面具有重大潜力.