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Carving out a Glycoside Hydrolase Active Site for Incorporation into a New Protein Scaffold Using Deep Network
Anders Lønstrup Hansen1, Frederik Friis Theisen1, Ramon Crehuet2
1The Linderstrøm-Lang Centre for Protein Science, Section for Biomolecular Sciences, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark.
ACS Synthetic Biology
|February 15, 2024
Summary
Computational design created simplified enzyme scaffolds, reducing size and maintaining stability. This offers a promising foundation for future enzyme engineering and industrial applications.
Area of Science:
- Protein engineering
- Computational biology
- Biocatalysis
Background:
- Enzymes are vital industrial biocatalysts but face limitations in stability and substrate specificity.
- Natural enzyme complexity hinders effective enzyme engineering.
- De novo design of simplified enzyme scaffolds is an emerging strategy.
Purpose of the Study:
- To design a simplified protein scaffold incorporating the active site of an endo-α-N-acetylgalactosaminidase.
- To explore the potential of scaffold hallucination for creating platforms for enzyme engineering.
Main Methods:
- Utilized trRosetta hallucination, deep learning-based structure prediction, and ProteinMPNN for sequence design.
- Designed a 290-amino acid protein scaffold.
- Expressed and characterized 11 designed proteins.
Main Results:
- Six of 11 designed proteins were soluble monomers with comparable or enhanced thermostability.
- Designed proteins were significantly smaller (over 100 kDa reduction) than the natural enzyme.
- Crystal structures of a representative design showed high accuracy (1.0 Å RMSD) compared to the computational model.
Conclusions:
- Scaffold hallucination is a viable strategy for designing simplified enzyme active site mimics.
- The designed scaffolds show potential as platforms for future enzyme engineering.
- Further optimization is needed to restore enzymatic activity.
