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

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.4K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
11.4K
Conservation of Protein Domains02:26

Conservation of Protein Domains

3.2K
3.2K
Protein Families02:47

Protein Families

15.8K
Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
15.8K
Conserved Binding Sites01:49

Conserved Binding Sites

4.4K
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...
4.4K
Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.3K
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...
13.3K

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

DAQplugin: Deep Learning based Real-time Model Evaluation Plugin for ChimeraX.

bioRxiv : the preprint server for biology·2026
Same author

Direct Detection and Atomic Modeling of Ligands in Cryo-EM Maps Using Deep Learning.

bioRxiv : the preprint server for biology·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors.

Journal of cheminformatics·2026
Same author

Multivalent recognition of ferritin by full-length NCOA4 enables robust ferritinophagy.

Protein science : a publication of the Protein Society·2026
Same author

MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.

Knowledge-based systems·2026
Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026
查看所有相关文章

相关实验视频

Updated: Sep 13, 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

2.0K

通过使用Domain-PFP使用Function-Aware域嵌入来预测蛋白质功能.

Nabil Ibtehaz1, Daisuke Kihara2,3

  • 1Department of Computer Science, Purdue University, West Lafayette, IN, USA.

Methods in molecular biology (Clifton, N.J.)
|July 29, 2025
PubMed
概括
此摘要是机器生成的。

域-PFP使用自主监督学习来创建功能性蛋白质域表示,改进了in-silico蛋白质功能预测. 这种方法克服了数据挑战,并优于现有的方法.

关键词:
深度学习是一种深度学习.基因本体学是基因的本体学.这就是InterPro的意义.蛋白质域表示学习学习蛋白质功能的预测和预测自主监督学习学习

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

68.9K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K

相关实验视频

Last Updated: Sep 13, 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

2.0K
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

68.9K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K

科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 结构生物学 结构生物学

背景情况:

  • 蛋白质功能预测是生物信息学的一个主要挑战.
  • 蛋白质域是关键的功能单位,但高维度和稀少性阻碍了预测.
  • 现有的方法难以处理大量的域名和有限的注释.

研究的目的:

  • 开发一种有效的in-silico蛋白功能预测方法.
  • 为了生成蛋白质域的功能意识表示.
  • 解决蛋白质生物信息学中高维度和稀疏性的挑战.

主要方法:

  • 利用自主监督学习来生成域表示.
  • 采用轻量级的浅层神经网络.
  • 捕获了蛋白质域和基因本体学 (GO) 术语之间的关联.

主要成果:

  • 开发了Domain-PFP用于功能信息化的域内嵌入.
  • 域内嵌入显示了重要的功能相关性.
  • 域-PFP在蛋白质功能预测方面表现优于当前最先进的方法.

结论:

  • 域-PFP有效地产生功能意识的域嵌入.
  • 该方法成功地解决了维度和稀疏性问题.
  • 一个用户友好的Google Colab网络服务可用于Domain-PFP分析.