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-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
Protein Networks02:26

Protein Networks

4.5K
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,...
4.5K
Ligand Binding Sites02:40

Ligand Binding Sites

14.8K
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...
14.8K
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

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

Toxicity-informed control of global PM<sub>2.5</sub> emissions.

National science review·2026
Same author

Crowded Public Spaces as Hotspots of Airborne Microbial Risk: A Population-Weighted Risk Assessment in Urban Environments.

Environmental science & technology·2026
Same author

Nasal Instillation of Complex Metal Oxide Particles Induces Brain Metal Accumulation and Neurobehavioral Toxicity in Mice.

Environmental science & technology·2026
Same author

Antibiotic Metabolites Are an Overlooked Driver of Resistance Dissemination in Plant Systems.

Environmental science & technology·2026
Same author

Steam Cooking Methods Promote the Transfer of Viable Antibiotic-Resistant Pathogens from Water into Air.

Environment & health (Washington, D.C.)·2026
Same author

Physicochemical Properties and Aging Behavior of Black Carbon across Emission Sources Determined by Char and Soot Subgroups.

Environmental science & technology·2026

相关实验视频

Updated: Jan 8, 2026

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

一种基于变压器的深度学习方法来预测空气有机污染物-人类蛋白质相互作用.

Yan Zhu1,2,3, Shihao Wang4, Yong Han4

  • 1Hong Kong Jockey Club STEM Laboratory of Genomics and AI in Healthcare, The Hong Kong Polytechnic University, Hong Kong 999077, China.

Environmental science & technology
|December 11, 2025
PubMed
概括

一个新的深度学习模型,tipFormer,准确地预测了污染物-蛋白质相互作用,进步了我们对空气污染的理解.

关键词:
空气污染 空气污染空气中的有机污染物-蛋白质相互作用.注意力机制注意力机制深度学习是一种深度学习.

更多相关视频

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K

相关实验视频

Last Updated: Jan 8, 2026

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
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K

科学领域:

  • 环境健康 环境健康
  • 计算生物学 计算生物学
  • 毒理学 毒理学 毒理学

背景情况:

  • 空气污染是全球主要的健康问题.
  • 了解污染物和蛋白质之间的分子相互作用对于评估毒性至关重要.
  • 目前的方法很难识别这些相互作用,特别是对于新污染物.

研究的目的:

  • 开发一种新的深度学习模型,用于预测空气中的有机污染物与蛋白质相互作用.
  • 加强对空气污染毒性的机制性理解和风险评估.

主要方法:

  • 开发了tipFormer,这是一个使用双重预训练语言模型和交叉注意力的深度学习模型.
  • 编码蛋白质和有机污染物以捕捉相互作用模式.
  • 在人类支气管上皮细胞中使用全基因组转录组分析验证的预测.

主要成果:

  • tipFormer在一个测试套件上实现了最先进的性能,AUC为0.9787.
  • 预测的污染物目标与实验性响应基因显著一致.
  • 证明了对空气污染影响的生物相关性和机械洞察力.

结论:

  • tipFormer提供了一种强大的计算方法来预测污染物-蛋白质相互作用.
  • 这项研究为空气污染毒性的分子基础提供了更深入的机械洞察力.
  • 这项工作将计算预测与实验验证联系起来,以改善风险评估.