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A Customizable Approach for the Enzymatic Production and Purification of Diterpenoid Natural Products
Published on: October 4, 2019
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Predictive Engineering of Class I Terpene Synthases Using Experimental and Computational Approaches.
Nicole G H Leferink1, Nigel S Scrutton1
1Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
Chembiochem : a European Journal of Chemical Biology
|October 20, 2021
Summary
Engineered microbes are key for producing diverse terpenoids. Advances in genome mining, computational modeling, and machine learning promise more predictable terpene synthase (TS) engineering for biomanufacturing.
Area of Science:
- Biotechnology and Natural Product Synthesis
- Enzymology and Protein Engineering
Background:
- Terpenoids are a diverse class of natural products with significant industrial applications.
- Engineered microbes offer a sustainable alternative to traditional extraction and chemical synthesis for terpenoid production.
- Terpene synthases (TSs) are crucial enzymes responsible for the structural diversity of terpenoids, but their complex function-product relationship poses challenges.
Purpose of the Study:
- To review recent advancements in methodologies for the engineering of terpene synthases (TSs).
- To highlight strategies that enable more predictive design of TSs for terpenoid biomanufacturing.
- To address the challenges associated with the functional plasticity and multi-product formation of TSs.
Main Methods:
- Genome mining for identifying novel terpene synthase (TS) genes.
- Computational modeling to understand TS active site dynamics and substrate interactions.
- High-throughput screening assays for characterizing TS activity and product profiles.
- Machine learning approaches for predicting TS function and guiding enzyme engineering.
Main Results:
- Recent advances provide new tools for exploring the functional diversity of terpene synthases (TSs).
- Integration of computational and experimental methods enhances the understanding of TS mechanisms.
- Machine learning models show potential for predicting enzyme function and improving engineering predictability.
Conclusions:
- Predictive engineering of terpene synthases (TSs) is becoming increasingly feasible through integrated approaches.
- These advancements are critical for optimizing the biomanufacturing of valuable terpenoids.
- Future research will likely focus on refining these methods for precise control over TS activity and product outcome.
