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Inside living organisms, enzymes act as catalysts for many biochemical reactions involved in cellular metabolism. The role of enzymes is to reduce the activation energies of biochemical reactions by forming complexes with its substrates. The lowering of activation energies favor an increase in the rates of biochemical reactions.
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Enzymes speed up reactions by lowering the activation energy of the reactants. The speed at which the enzyme turns reactants into products is called the rate of reaction. Several factors impact the rate of reaction, including the number of available reactants. Enzyme kinetics is the study of how an enzyme changes the rate of a reaction.
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An Automated Data-Driven Pipeline for Improving Heterologous Enzyme Expression.

Emily E Wrenbeck, Matthew A Bedewitz, Justin R Klesmith

  • 1Department of Biomedical Engineering , University of Virginia , Charlottesville , Virginia 22903 , United States.

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|February 6, 2019
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Summary

Researchers developed an automated protein engineering method to stabilize enzymes for improved function. This strategy enhances the expression of difficult-to-express enzymes, crucial for biotechnology.

Keywords:
deep mutational scanningenzyme stabilityheterologous pathway expressionhigh-throughput screeningpolyketide synthasetropane alkaloids

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Area of Science:

  • Biochemistry
  • Protein Engineering
  • Synthetic Biology

Background:

  • Enzymes drive essential chemical reactions but often exhibit poor stability, limiting their use.
  • Modifications to enhance enzyme stability can negatively impact catalytic activity.
  • Developing stable and functional enzymes is critical for biotechnological applications.

Purpose of the Study:

  • To create an automated protein engineering strategy for stabilizing enzymes while preserving their catalytic function.
  • To improve the functional expression of a specific enzyme involved in tropane alkaloid biosynthesis.

Main Methods:

  • Utilized deep mutational scanning coupled with multiple-filter screening and combinatorial mutagenesis.
  • Employed an automated protein engineering approach to identify stabilizing mutations.
  • Validated the strategy on a Type III polyketide synthase from Atropa belladonna.

Main Results:

  • Developed an enzyme variant with 8 mutations showing over 25-fold improved activity in E. coli cell lysates.
  • Achieved an 11.5 °C increase in melting temperature with minimal loss of catalytic efficiency.
  • Demonstrated the effectiveness of the multiple-filter approach with homology modeling.

Conclusions:

  • The automated protein engineering strategy successfully enhances enzyme stability and functional expression.
  • This method provides a valuable tool for improving difficult-to-express enzymes for biotechnology.
  • The approach offers a balance between enhanced stability and retained catalytic performance.