ArticleJournal of the American Chemical Society2025
Enzyme Enhancement Through Computational Stability Design Targeting NMR-Determined Catalytic Hotspots.
Article in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Emergence of specific binding and catalysis from a designed generalist binding protein.Nature chemistry · 2026Article
- Substrate scope of ancestral versus modern family-1 glycosidases.Protein science : a publication of the Protein Society · 2026Article
- Structure-activity relationships in catalytic amyloids.Journal of inorganic biochemistry · 2026Article
- Mapping functional dynamics hotspots for protein engineering with NMR peak intensity analysis.Protein engineering, design & selection : PEDS · 2026Article
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
- Towards site-specific information on PET degrading enzymes using NMR near operational temperature.Scientific reports · 2025Article
- A method for predicting enzyme substrate specificity residues using homologous sequence information.Protein science : a publication of the Protein Society · 2025Article
- Article
- Highly efficient enzymes designed from scratch.Nature · 2025Article
- Review
- Efficient Searches in Protein Sequence Space Through AI-Driven Iterative Learning.International journal of molecular sciences · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enzymes are the quintessential green catalysts, but realizing their full potential for biotechnology typically requires improvement of their biomolecular properties. Catalysis enhancement, however, is often accompanied by impaired stability. Here, we show how the interplay between activity and stability in enzyme optimization can be efficiently addressed by coupling two recently proposed methodologies for guiding directed evolution. We first identify catalytic hotspots from chemical shift perturbations induced by transition-state-analogue binding and then use computational/phylogenetic design (FuncLib) to predict stabilizing combinations of mutations at sets of such hotspots. We test this approach on a previously designed de novo Kemp eliminase, which is already highly optimized in terms of both activity and stability. Most tested variants displayed substantially increased denaturation temperatures and purification yields. Notably, our most efficient engineered variant shows a ∼3-fold enhancement in activity (
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Registered trials
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