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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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Related Experiment Video

Updated: Oct 19, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Exploiting enzyme evolution for computational protein design.

Gaspar P Pinto¹, Marina Corbella¹, Andrey O Demkiv¹

¹Department of Chemistry - BMC, Uppsala University, BMC Box 576, S-751 23 Uppsala, Sweden.

Trends in Biochemical Sciences

|September 21, 2021

Summary

This summary is machine-generated.

Evolutionary insights enhance computational enzyme design by reconstructing ancestral sequences, mimicking natural dynamics, and enabling modular design. This approach advances the creation of novel enzymes with tailored properties.

Keywords:

conformational dynamics directed evolution enhanced sampling approaches residue coevolution structural bioinformatics

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Area of Science:

Biochemistry and Molecular Biology
Computational Biology
Evolutionary Biology

Background:

Growing interest in enzyme evolution and computational enzyme design.
Need to understand physicochemical parameters driving enzyme evolution.
Advances in computational methods for enzyme design.

Purpose of the Study:

To review how evolutionary information can be integrated into enzyme design.
To highlight three key areas: ancestral sequence reconstruction, conformational dynamics, and modular design.
To showcase examples of successful enzyme design incorporating evolutionary data.

Main Methods:

Review of existing literature and case studies.
Discussion of ancestral sequence reconstruction (ASR) for generating design starting points.
Analysis of natural enzyme evolution, conformational dynamics, and modular protein assembly.

Main Results:

Demonstration of ASR's utility in creating novel enzyme scaffolds.
Insights into mimicking natural conformational dynamics for in silico design.
Examples of successful modular enzyme design inspired by protein fold evolution.

Conclusions:

Evolutionary information is crucial for advancing computational enzyme design.
Integrating ASR, dynamics, and modularity offers powerful strategies for creating new enzymes.
Future enzyme design efforts should leverage evolutionary principles for greater innovation.