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

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

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

Association Between Hormone Therapy and Health-Related Quality of Life in Postmenopausal Korean Women: A Nationwide Cross-Sectional Study Using 2005-2009 KNHANES Data.

Healthcare (Basel, Switzerland)·2026
Same author

Inhibiting 15-PGDH restores redox homeostasis and confers neuroprotection in Parkinson's disease.

Redox biology·2026
Same author

Trends in menopausal hormone therapy use among postmenopausal women in South Korea: analysis of KNHANES 2005-2024.

Frontiers in public health·2026
Same author

Changes in C-reactive protein levels over time in high-temperature environments using postmortem blood.

Forensic science international·2026
Same author

Roll-to-Roll Gravure-Printed SWCNT Ring Oscillator for Flexible Microfluidic Ion Sensing.

Nanomaterials (Basel, Switzerland)·2026
Same author

VectorSage: enhancing PubMed article retrieval with advanced semantic search.

Bioinformatics advances·2026

相关实验视频

Updated: Jul 25, 2025

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

15.5K

AiKPro:使用基于结构的序列对齐和分子3D符合组合描述器进行基因组范围的生物活性分析的深度学习模型.

Hyejin Park1, Sujeong Hong1, Myeonghun Lee1

  • 1AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea.

Scientific reports
|June 24, 2023
PubMed
概括

深度学习模型AiKPro使用序列和结构数据准确地预测酶-连接体结合亲缘关系. 该工具有助于发现选择性激酶抑制剂用于疾病治疗.

更多相关视频

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

384
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

68.8K

相关实验视频

Last Updated: Jul 25, 2025

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

15.5K
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

384
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

68.8K

科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 药物发现 药物发现 药物发现

背景情况:

  • 激酶抑制剂对于治疗疾病至关重要,但它们的发展受到激酶结构相似性所阻碍.
  • 有效的全基因组生物活性分析对于理解基因酶功能和识别选择性抑制剂至关重要.

研究的目的:

  • 开发AiKPro,这是一个深度学习模型,用于预测酶-连接体结合亲缘关系.
  • 评估AiKPro在预测已知和新激酶和化合物的相互作用方面的表现.

主要方法:

  • AiKPro集成了结构验证的多个序列对齐和3D分子符合组合描述器.
  • 基于注意力的机制被用于模拟酶 - 连接体相互作用.
  • 用皮尔森的相关系数和激酶活性分析来评估模型的性能.

主要成果:

  • AiKPro实现了高预测准确度,Pearson的相关系数为0.88 (测试组) 和0.87 (未训练的化合物).
  • 该模型显示了强度和良好的酶活性概况在整个基因组.
  • 这些结果表明AiKPro在识别新型相互作用和选择性抑制剂方面的潜力.

结论:

  • AiKPro提供了一种强大的计算方法,用于预测酶-连接体结合亲缘关系.
  • 该模型有助于发现新的选择性激酶抑制剂.
  • AiKPro可以指导用于酶向治疗的合理药物设计.