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Updated: Oct 4, 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

Enhancing computational enzyme design by a maximum entropy strategy.

Wen Jun Xie¹, Mojgan Asadi², Arieh Warshel¹

¹Department of Chemistry, University of Southern California, Los Angeles, CA 90089-1062 xwj123@gmail.com warshel@usc.edu.

Proceedings of the National Academy of Sciences of the United States of America

|February 9, 2022

Summary

This summary is machine-generated.

Statistical energy from homologous sequences, using the maximum entropy (MaxEnt) principle, strongly correlates with enzyme catalysis and stability. This finding offers a new approach for computational enzyme design.

Keywords:

catalysis enzyme design evolution maximum entropy

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Area of Science:

Computational biology
Biophysics
Enzyme engineering

Background:

Computational enzyme design is crucial but physics-based methods show slow progress.
Machine learning approaches for predicting enzyme catalytic power are not yet established.

Purpose of the Study:

To investigate the correlation between statistical energy derived from homologous sequences and enzyme catalytic activity and stability.
To explore the potential of the maximum entropy (MaxEnt) principle in guiding enzyme design.

Main Methods:

Inferred statistical energy from homologous enzyme sequences using the maximum entropy (MaxEnt) principle.
Correlated inferred statistical energy with experimental data on enzyme catalysis and stability.

Main Results:

A significant correlation was found between statistical energy and enzyme catalysis at the active site.
Statistical energy also correlated with enzyme stability in regions distant from the active site.
The findings decode enzyme architecture and link enzyme evolution to physical chemistry.

Conclusions:

The maximum entropy (MaxEnt) principle provides a powerful tool for understanding enzyme function and guiding enzyme design.
This approach deepens the understanding of the enzyme stability-activity trade-off.
The study offers a novel connection between evolutionary principles and the physical chemistry of enzyme catalysis.