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

Ligand Binding and Linkage00:49

Ligand Binding and Linkage

5.4K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
5.4K

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

Membrane protein solubilization and structure determination using de novo-designed proteins.

Science (New York, N.Y.)·2026
Same author

Controlling metal-carbonate phase, form, and function through de novo protein design.

bioRxiv : the preprint server for biology·2026
Same author

Improved Stability and Brightness Following Iterative Redesign of a De Novo Biliprotein.

Biochemistry·2026
Same author

Programmed synthesis of mesoporous protein crystals in cellular reactors.

Nature nanotechnology·2026
Same author

Generative design of programmable asymmetric β-barrel nanopores.

bioRxiv : the preprint server for biology·2026
Same author

Why machine learning fails at mass spectrometry for small molecules.

Nature metabolism·2026
Same journal

Efficient evidence-based genome annotation with EviAnn.

Nature methods·2026
Same journal

ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.

Nature methods·2026
Same journal

RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.

Nature methods·2026
Same journal

Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution.

Nature methods·2026
Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026
Same journal

Cloud-based microscope enables live neuroimaging for 24 h and beyond with worldwide access.

Nature methods·2026
查看所有相关文章

相关实验视频

Updated: Jan 9, 2026

Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

11.2K

使用RFdiffusion2进行原子级酶活性部位的支架.

Woody Ahern1,2,3, Jason Yim4,5, Doug Tischer1,2

  • 1Department of Biochemistry, University of Washington, Seattle, WA, USA.

Nature methods
|December 3, 2025
PubMed
概括
此摘要是机器生成的。

一个新的AI模型RFdiffusion2直接从化学反应中设计新的酶. 这通过绕过以前计算方法的局限性来推进de novo酶设计.

更多相关视频

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

903
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.9K

相关实验视频

Last Updated: Jan 9, 2026

Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

11.2K
Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

903
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.9K

科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 蛋白质工程是指蛋白质工程.

背景情况:

  • 传统的酶设计依赖于理想化的功能组安排和复杂的蛋白质结构生成.
  • 现有的酶设计人工智能方法通常需要预定义的残留位置和有限的灵活性.

研究的目的:

  • 引入RoseTTAFold扩散2 (RFdiffusion2),这是一个深度生成模型,用于新的酶设计.
  • 通过从功能组几何形状直接生成,克服当前基于人工智能的酶设计的局限性.

主要方法:

  • 开发了RFdiffusion2,一种用于酶设计的深度生成模型.
  • 使用函数组几何作为直接输入,绕过残留顺序规范和反向旋转器生成.
  • 在多个活跃站点上对现有方法进行RFdiffusion2的基准测试.

主要成果:

  • RFdiffusion2成功地为基准集中的所有41个活跃站点生成了支架,显著优于以前的方法 (16个活跃站点).
  • 为三个不同的催化机制设计了新型酶.
  • 识别了活性酶候选物,每种机制的实验试验少于96.

结论:

  • RFdiffusion2展示了一种强大的新方法,用于 de novo 酶的创造.
  • 原子级生成模型提供了从反应机制到功能性酶设计的直接途径.
  • 这种方法显著提高了计算酶工程的效率和范围.