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Computational Stabilization of a Non-Heme Iron Enzyme Enables Efficient Evolution of New Function
Brianne R King1, Kiera H Sumida1,2, Jessica L Caruso1
1Department of Chemistry, University of Washington, Seattle, Washington, 98195, USA.
Angewandte Chemie (International Ed. in English)
|October 12, 2024
Summary
Deep learning protein redesign using ProteinMPNN enhances enzyme stability and function. Stabilized enzymes are more efficiently evolved, yielding greater activity for industrial biocatalysis.
Area of Science:
- Biocatalysis and enzyme engineering
- Protein design and computational biology
- Synthetic biology and industrial biotechnology
Background:
- Deep learning tools for enzyme design are emerging, necessitating evaluation in engineering workflows.
- The Fe(II)/αKG superfamily of enzymes offers diverse catalytic functions for potential biocatalyst applications.
- Directed evolution is a key strategy for optimizing enzyme properties.
Purpose of the Study:
- To evaluate the effectiveness of the deep learning tool ProteinMPNN for redesigning Fe(II)/αKG enzymes.
- To improve enzyme stability, solubility, and expression while retaining native and non-native functions.
- To demonstrate enhanced directed evolution efficiency using stabilized enzyme variants.
Main Methods:
- Utilized ProteinMPNN for redesigning Fe(II)/αKG enzymes, focusing on stability and function.
- Performed systematic comparisons of directed evolution trajectories for wild-type and stabilized enzymes.
- Identified structural and sequence constraints to preserve catalytic function during redesign.
Main Results:
- ProteinMPNN successfully redesigned Fe(II)/αKG enzymes for enhanced stability, solubility, and expression.
- Stabilized enzyme variants showed more efficient evolution, achieving an 80-fold activity increase compared to a 6-fold increase for the wild-type.
- The ProteinMPNN-based stabilization approach was generalizable to other Fe(II)/αKG superfamily members.
Conclusions:
- Deep learning tools like ProteinMPNN can be routinely implemented in directed evolution workflows.
- ProteinMPNN facilitates the engineering of novel biocatalysts with improved properties for industrial applications.
- This framework enables the development of stabilized, catalytically active enzymes for diverse biotechnological uses.
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