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

Methods of Medium Optimization01:28

Methods of Medium Optimization

68
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
68

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

Multi-Level Engineering of Bacillus Subtilis for High-Level Extracellular Production of Functional Expansins.

Biotechnology journal·2026
Same author

Multidimensional Engineering of Saccharomyces cerevisiae for Efficient Production of Retinol.

Biotechnology journal·2026
Same author

Engineering Programmable Tryptophan-Responsive Biosensors Based on RNA-Binding Attenuation Protein for Strain Optimization.

ACS synthetic biology·2026
Same author

Combinatorial Engineering of <i>Escherichia coli</i> for Enhancing Lipoic Acid Production.

ACS synthetic biology·2026
Same author

Enhanced Menaquinone Biosynthesis by Engineering 2-Succinyl-5-enolpyruvyl-6-hydroxy-3-cyclohexadiene-1-carboxylate Synthase MenD to Alleviate Feedback Inhibition in <i>Bacillus subtilis</i>.

ACS synthetic biology·2025
Same author

Biosensor-Assisted Multitarget Gene Fine-Tuning for <i>N</i>-Acetylneuraminic Acid Production in <i>Escherichia coli</i> with Sole Carbon Source Glucose.

Journal of agricultural and food chemistry·2025

相关实验视频

Updated: Apr 24, 2026

Defining Substrate Specificities for Lipase and Phospholipase Candidates
08:59

Defining Substrate Specificities for Lipase and Phospholipase Candidates

Published on: November 23, 2016

15.0K

用"设计-构建-测试-学习"框架重塑酸酶基质对受控生物合成的偏好.

Jiangong Lu1,2, Xueqin Lv1,2, Wenwen Yu1,2

  • 1Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|March 20, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种量子力学引导的框架,用于设计N-乙糖胺 (GlcNAc) 生物合成的酶. 优化的酶在生物反应器中实现了创纪录的高GlcNAc产量,证明了工业生物催化剂的强大方法.

关键词:
N-乙葡萄糖胺-6-酸盐设计构建测试学习框架.酸酶是一种酸酶.蛋白质工程工程 蛋白质工程基质偏好 基质偏好

更多相关视频

Assessing Cellular Target Engagement by SHP2 PTPN11 Phosphatase Inhibitors
08:45

Assessing Cellular Target Engagement by SHP2 PTPN11 Phosphatase Inhibitors

Published on: July 17, 2020

6.2K
Author Spotlight: Developing Tools to Tune the Activity of Tyrosine Phosphatases
06:56

Author Spotlight: Developing Tools to Tune the Activity of Tyrosine Phosphatases

Published on: September 6, 2024

366

相关实验视频

Last Updated: Apr 24, 2026

Defining Substrate Specificities for Lipase and Phospholipase Candidates
08:59

Defining Substrate Specificities for Lipase and Phospholipase Candidates

Published on: November 23, 2016

15.0K
Assessing Cellular Target Engagement by SHP2 PTPN11 Phosphatase Inhibitors
08:45

Assessing Cellular Target Engagement by SHP2 PTPN11 Phosphatase Inhibitors

Published on: July 17, 2020

6.2K
Author Spotlight: Developing Tools to Tune the Activity of Tyrosine Phosphatases
06:56

Author Spotlight: Developing Tools to Tune the Activity of Tyrosine Phosphatases

Published on: September 6, 2024

366

科学领域:

  • 生物催化剂和代谢工程
  • 计算化学和酶设计
  • 合成生物学用于工业应用.

背景情况:

  • 酶工程对于优化生物催化剂至关重要,但通常依赖于低效的试错方法.
  • 为特定的代谢途径开发强大的酶,如N-乙葡萄糖胺 (GlcNAc) 生物合成,对于工业应用至关重要.
  • 自然酶的有限的催化效率和基质特异性阻碍了它们在微生物细胞工厂中的使用.

研究的目的:

  • 开发一个合理的酶设计框架,结合量子力学 (QM) 进行增强的生物催化.
  • 为了改造酸酶BT4131以改善N-乙糖胺-6-酸盐 (GlcNAc6P) 基质偏好和催化活性.
  • 为了证明该框架在实现大规模生物反应器中高度GlcNAc生产方面的有效性.

主要方法:

  • 使用了设计-构建-测试-学习 (DBTL) 循环,与QM计算和分子建模集成.
  • 在最初的突变设计中采用了基于力场的方法 (例如,M1 (L129Q)).
  • 执行代计算机辅助设计以稳定过渡状态并优化酶性能.

主要成果:

  • 工程突变M1显示,由于激活能量的显著减少,基质对GlcNAc6P的偏好增加了1.4倍.
  • 开发出突变M4 (I49Q/L129Q/G172L),对GlcNAc6P的催化效率提高了9.5倍,对Glc6P的活性降低了.
  • 在50L生物反应器中,在0.597g (g葡萄糖) -1的产量下,达到217.3g L-1的N-乙糖胺标位记录.

结论:

  • 集成于QM的DBTL框架允许合理的酶设计,以改善工业生物催化剂.
  • 工程化酸酶显著提高了N-乙葡萄糖胺生物合成效率.
  • 这种方法为开发工业可行的生物催化剂和优化代谢途径提供了一个强大的战略.