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Updated: Jul 19, 2025
A Customizable Approach for the Enzymatic Production and Purification of Diterpenoid Natural Products
Published on: October 4, 2019
QM/MM Modeling Aided Enzyme Engineering in Natural Products Biosynthesis.
Fan Zhang1,2, Tao Zeng2, Ruibo Wu2
1Guangdong Provincial Key Laboratory of Translational Cancer Research of Chinese Medicines, Joint International Research Laboratory of Translational Cancer Research of Chinese Medicines, International Institute for Translational Chinese Medicine, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China.
Quantum mechanics/molecular mechanics (QM/MM) computational methods accelerate the engineering of natural product biosynthesis. This review highlights QM/MM
Area of Science:
- Biochemistry and Biotechnology
- Computational Chemistry
- Drug Discovery
Background:
- Natural products are vital in pharmaceuticals, but demand outstrips supply.
- Engineered biosynthesis offers a sustainable alternative for natural product production.
- Enzyme engineering is crucial for optimizing biocatalysis but requires advanced tools.
Purpose of the Study:
- To review recent advances in QM/MM computational investigations for enzyme catalysis.
- To explore the application of QM/MM in enzyme engineering for natural product biosynthesis.
- To discuss future challenges and perspectives in this field.
Main Methods:
- Quantum mechanics/molecular mechanics (QM/MM) computational simulations.
- Analysis of enzyme catalysis mechanisms.
- Rational enzyme engineering strategies.
Main Results:
- QM/MM has become a powerful tool for understanding enzyme mechanisms.
- QM/MM aids in the rational design of enzymes for improved catalytic efficiency.
- Significant progress has been made in applying QM/MM to natural product biosynthesis.
Conclusions:
- QM/MM is instrumental in advancing enzyme engineering for natural product synthesis.
- Further development of QM/MM methods will enhance biocatalysis and drug discovery.
- Interdisciplinary approaches combining computation and experimentation are key for future success.
