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 Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

2.6K
Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...
2.6K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.2K
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...
13.2K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

3.8K
3.8K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

710
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
710
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.3K
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...
11.3K
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

1.7K
1.7K

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

NicheTrans: spatial-aware cross-omics translation.

Nature methods·2026
Same author

Causal effects of wildfire PM<sub>2.5</sub> on hospital costs and length of stay in Brazil.

Nature communications·2026
Same author

Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.

Briefings in bioinformatics·2026
Same author

Multimodal Information-Driven Heterogeneous Graph Neural Networks for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling·2026
Same author

HLABrew for Human Leukocyte Antigen Class I-Presented Epitope Recognition and Mimotope Discovery.

Journal of chemical information and modeling·2026
Same author

Modification-aware AI enables terminal chemical modifications for peptide design and discovers potent antimicrobials.

bioRxiv : the preprint server for biology·2026

相关实验视频

Updated: Sep 10, 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

BiVAE-CPI:使用双边变异自编码器预测化合物-蛋白质相互作用的可解释生成模型

Yongxin Zhu1, Jianxin Wang1, Shiyue He1

  • 1School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China.

Journal of chemical information and modeling
|August 21, 2025
PubMed
概括

BiVAE-CPI是一种新的深度学习模型,通过考虑CPI对之间的相关性和学习共享潜伏表征来改善化合物-蛋白质相互作用 (CPI) 的预测. 这种方法提高了药物发现效率.

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 10, 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:

  • 计算化学
  • 生物信息学
  • 药物发现

背景情况:

  • 预测化合物-蛋白相互作用 (CPI) 对于药物发现至关重要,但与传统方法相比耗时.
  • 深度学习模型越来越多地用于CPI预测,但许多模型未能有效地捕捉对对相关性或潜在表示.

研究的目的:

  • 提出一个新的深度学习模型BiVAE-CPI,用于提高CPI预测.
  • 通过结合CPI对之间的相关性和学习共享的低维隐藏表示来解决现有方法的局限性.

主要方法:

  • 使用双边变量自编码器 (BiVAE) 来建模相关性和学习潜伏表示.
  • 用于复合表示学习的使用图形同态网络 (GIN).
  • 使用一个封闭的卷积编码器嵌入蛋白质序列.

主要成果:

  • BiVAE-CPI在基准数据集上表现优于最先进的方法,特别是在不平衡的数据上.
  • 该模型有效地捕获相关性,并学习共享的潜在表征以进行CPI预测.
  • 连续隐藏空间表示提供了更好的解释性,并将分布与特征融合在一起.

结论:

  • 在CPI预测准确性和效率方面,BiVAE提供了显著的进步.
  • 考虑相关性和共享隐性表示对于开发可靠的CPI预测模型是有益的.
  • 这种方法有助于加速药物发现和开发.