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Physics-based modeling in the new era of enzyme engineering
Christopher Jurich1, Qianzhen Shao1, Xinchun Ran1
1Department of Chemistry, Vanderbilt University, Nashville, TN, USA.
Nature Computational Science
|April 24, 2025
Summary
Computational enzyme engineering is advancing with AI and bioinformatics. Physics-based modeling, like molecular and quantum mechanics, complements these methods for enhanced enzyme design and function. This perspective explores its potential and challenges.
Area of Science:
- Biochemistry
- Computational Biology
- Enzyme Engineering
Background:
- Enzyme engineering is increasingly leveraging computational strategies.
- Bioinformatics and artificial intelligence (AI) accelerate mutant screening.
- Physics-based modeling methods are crucial for many enzyme engineering objectives.
Purpose of the Study:
- To highlight the role of physics-based modeling in computational enzyme engineering.
- To explore current developments, challenges, and opportunities in this field.
- To emphasize how these methods can unlock the full potential of computational enzyme design.
Main Methods:
- Review of current physics-based modeling techniques (e.g., molecular mechanics, quantum mechanics).
- Analysis of the integration of computational strategies in enzyme engineering.
- Discussion of bioinformatics and AI applications in mutant screening.
Main Results:
- Physics-based modeling is an essential complement to AI and bioinformatics in enzyme engineering.
- Significant progress has been made in computational tool development.
- Key challenges and future opportunities for physics-based modeling have been identified.
Conclusions:
- Physics-based modeling is vital for advancing computational enzyme engineering.
- Further tool development is needed to address unmet challenges.
- Integrating these methods will accelerate the design of novel enzymes with enhanced functions.
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