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Defining Substrate Specificities for Lipase and Phospholipase Candidates
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Reshaping Phosphatase Substrate Preference for Controlled Biosynthesis Using a "Design-Build-Test-Learn" Framework.

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
Summary
This summary is machine-generated.

This study introduces a quantum mechanics-guided framework to engineer enzymes for N-acetylglucosamine (GlcNAc) biosynthesis. The optimized enzyme achieved record-high GlcNAc production in bioreactors, demonstrating a powerful approach for industrial biocatalysis.

Keywords:
N‐acetylglucosamine‐6‐phosphatedesign–build–test–learn frameworkphosphataseprotein engineeringsubstrate preference

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Area of Science:

  • Biocatalysis and metabolic engineering
  • Computational chemistry and enzyme design
  • Synthetic biology for industrial applications

Background:

  • Enzyme engineering is crucial for optimizing biocatalysis but often relies on inefficient trial-and-error methods.
  • Developing robust enzymes for specific metabolic pathways, like N-acetylglucosamine (GlcNAc) biosynthesis, is essential for industrial applications.
  • Limited catalytic efficiency and substrate specificity of natural enzymes hinder their use in microbial cell factories.

Purpose of the Study:

  • To develop a rational enzyme design framework incorporating quantum mechanics (QM) for enhanced biocatalysis.
  • To engineer the phosphatase BT4131 for improved N-acetylglucosamine-6-phosphate (GlcNAc6P) substrate preference and catalytic activity.
  • To demonstrate the framework's efficacy in achieving high-titer GlcNAc production in a large-scale bioreactor.

Main Methods:

  • Utilized a design-build-test-learn (DBTL) cycle integrated with QM calculations and molecular modeling.
  • Employed force field-based methods for initial mutant design (e.g., M1 (L129Q)).
  • Performed iterative computer-aided design to stabilize transition states and optimize enzyme performance.

Main Results:

  • Engineered mutant M1 showed a 1.4-fold increase in substrate preference for GlcNAc6P due to a significant reduction in activation energy.
  • Developed mutant M4 (I49Q/L129Q/G172L) with a 9.5-fold enhancement in catalytic efficiency for GlcNAc6P and reduced activity on Glc6P.
  • Achieved a record N-acetylglucosamine titer of 217.3 g L⁻¹ with a yield of 0.597 g (g glucose)⁻¹ in a 50-L bioreactor.

Conclusions:

  • The QM-incorporated DBTL framework enables rational enzyme design for improved industrial biocatalysis.
  • The engineered phosphatase significantly enhances N-acetylglucosamine biosynthesis efficiency.
  • This approach offers a powerful strategy for developing industrially viable biocatalysts and optimizing metabolic pathways.