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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
AI-redesigned starting points and outcomes enhance protein evolution
Nicholas A Krasnow1,2,3, Joy A Xu1,2,3, Emily Zhang1,2,3
1Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Nature
|July 22, 2026
Summary
Artificial intelligence (AI) redesigned botulinum neurotoxin (BoNT) proteases show enhanced stability and activity. AI-redesigned enzymes serve as superior starting points for protein evolution, yielding improved functional properties and specificity.
Area of Science:
- Protein engineering and computational biology.
- Enzyme evolution and directed evolution.
- Biotechnology and biopharmaceutical development.
Background:
- Engineered proteins often exhibit limitations in stability, activity, or specificity.
- Experimental enzyme evolution faces challenges that can be addressed by computational approaches.
- Botulinum neurotoxin (BoNT) proteases are a relevant class of enzymes for therapeutic and research applications.
Purpose of the Study:
- To apply artificial intelligence (AI)-based protein sequence design to create improved starting points for enzyme evolution.
- To investigate the mutational robustness and evolutionary potential of AI-redesigned enzymes compared to wild-type (WT) counterparts.
- To evolve AI-redesigned BoNT proteases for enhanced specificity towards a therapeutic target.
Main Methods:
- Utilized the ProteinMPNN model for AI-driven redesign of three distinct BoNT proteases.
- Conducted parallel phage-assisted continuous evolution campaigns using both AI-redesigned and WT proteases.
- Assessed catalytic efficiency, stability, and substrate specificity of evolved variants.
Main Results:
- AI-redesigned proteases demonstrated improved stability and retained full catalytic efficiency.
- Evolutionary campaigns initiated with redesigned enzymes consistently yielded proteases with higher activity and robustness.
- Redesigned starting points facilitated access to highly functional sequences not achievable from WT backgrounds.
- Evolved AI-redesigned BoNT/E protease achieved >79-fold greater specificity for ataxin-2 compared to WT-evolved variants.
Conclusions:
- AI-redesigned enzymes provide superior starting points for directed evolution, leading to enhanced enzyme properties.
- This AI-driven workflow offers a practical approach to engineer enzymes with improved stability, activity, and specificity.
- The findings have broad implications for advancing protein science and developing novel biocatalysts and therapeutics.
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