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

Antimicrobial Proteins01:23

Antimicrobial Proteins

5.0K
Antimicrobial proteins are important components of the immune system. They aid the body in combating pathogens by either killing them directly or hindering their replication processes. Four main types of antimicrobial substances are interferons, the complement system, iron-binding proteins, and antimicrobial proteins.
Interferons
Interferons (IFNs) are proteins produced by lymphocytes, macrophages, and fibroblasts infected with viruses. While IFNs cannot prevent viruses from entering and...
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

A Tutorial on Dimensionality Reduction and Clustering for Molecular Dynamics Trajectories: From Linear Algorithms to Deep Learning.

The journal of physical chemistry. B·2026
Same author

BFEE-Docking: A User-Friendly and Customizable End-to-End Tool from High-Throughput Virtual Screening to Binding Free-Energy Calculations.

Journal of chemical theory and computation·2026
Same author

Convergence is not correctness: context-dependent performance of enhanced-sampling methods across biological complexity.

Nature communications·2026
Same author

Aromatic ring flips reveal reshaping of protein dynamics in crystals and complexes.

Nature chemistry·2026
Same author

A Triple-Perception Adaptive Network for In Vivo Organ Recognition Using Diffuse Reflectance Hyperspectral Imaging.

Analytical chemistry·2026
Same author

Correction to "One for All, All for One: A Unified Framework for Free-Energy Calculations".

Accounts of chemical research·2026

相关实验视频

Updated: Sep 9, 2025

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

333

从人工智能驱动的序列生成到分子模拟:抗菌发现的全面框架

Chunsuo Tian1, Yuelei Hao1, Haohao Fu1,2

  • 1Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China.

Journal of chemical information and modeling
|August 29, 2025
PubMed
概括

研究人员开发了一种结合深度学习和分子模拟的新计算方法,以发现新的抗菌 (AMP). 这种方法成功地发现了两种有效的AMP对抗耐药细菌,为未来的药物发现提供了成本有效的策略.

更多相关视频

Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly
09:34

Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly

Published on: February 6, 2020

7.4K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

相关实验视频

Last Updated: Sep 9, 2025

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

333
Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly
09:34

Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly

Published on: February 6, 2020

7.4K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

科学领域:

  • 计算化学和药物发现
  • 抗菌的研究
  • 生物信息学和机器学习应用

背景情况:

  • 细菌对现有抗生素的耐药性是一个日益增长的全球健康威胁.
  • 抗微生物 (AMP) 是有前途的,但在临床转化方面面临挑战.
  • 深度学习为设计新的AMP提供了新的途径.

研究的目的:

  • 开发和验证用于系统的AMP设计和选的综合计算框架.
  • 确定具有潜在抗菌活性的新型AMP候选物.
  • 建立一个具有成本效益的AMP发现战略.

主要方法:

  • 使用基于字符串的生成对抗网络 (GAN) 来生成候选AMP序列.
  • 使用PGAT-ABPp区分网络和物理化学分析进行初步选.
  • 进行了分子动力学模拟,以评估膜相互作用,并合成了用于体外测试的有希望的候选物.

主要成果:

  • 产生了50个候选AMP序列,在初始查后确定了9个潜在候选.
  • 分子模拟表明两种选定的在细菌膜中形成水孔.
  • 在实验室中,合成的对克拉姆阴性 (大肠杆菌) 和克拉姆阳性 (黄金杆菌) 细菌表现出有效性.

结论:

  • 综合计算框架成功发现了两种新的临床相关的抗菌.
  • 已发现的AMP对关键的细菌病原体具有广泛的活性.
  • 这项研究验证了用于加速AMP发现的具有成本效益和广泛适用的计算策略.