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Catalytically Perfect Enzymes

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The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.

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Updated: Jun 13, 2025

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

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Published on: January 16, 2016

Enzyme Enhancement Through Computational Stability Design Targeting NMR-Determined Catalytic Hotspots.

Luis I Gutierrez-Rus¹, Eva Vos², David Pantoja-Uceda³

¹Departamento de Química Física, Facultad de Ciencias, Unidad de Excelencia de Química Aplicada a Biomedicina y Medioambiente (UEQ), Universidad de Granada, Granada 18071, Spain.

Journal of the American Chemical Society

|March 19, 2025

Summary

This summary is machine-generated.

Researchers enhanced enzyme engineering by combining two directed evolution methods. This approach improved enzyme activity and stability, creating highly efficient biocatalysts for biotechnology and anthropogenic reactions.

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Published on: December 25, 2021

Area of Science:

Biocatalysis
Protein Engineering
Directed Evolution

Background:

Enzymes are green catalysts crucial for biotechnology but often require property improvements.
Enhancing enzyme catalysis can paradoxically decrease stability, posing a challenge in enzyme optimization.

Purpose of the Study:

To develop an efficient strategy for enzyme optimization by coupling activity and stability improvements.
To create novel biocatalysts with tailored physicochemical properties for anthropogenic reactions.

Main Methods:

Identified catalytic hotspots using chemical shift perturbations from transition-state-analogue binding.
Employed computational/phylogenetic design (FuncLib) to predict stabilizing mutations at hotspots.
Tested the approach on a de novo Kemp eliminase, a highly optimized enzyme.

Main Results:

Engineered variants showed increased denaturation temperatures and purification yields.
Achieved a ~3-fold activity enhancement in the most efficient variant, surpassing previously designed enzymes.
Molecular simulations revealed enhanced catalysis due to eliminating inefficient substrate conformations.

Conclusions:

Dynamically guided enzyme engineering is a powerful design principle for novel biocatalysts.
The developed computational tools are effective for both de novo and natural enzyme engineering.
This strategy successfully balances enzyme activity and stability for advanced biotechnological applications.