ArticleJournal of the American Chemical Society2023
Using Data Science for Mechanistic Insights and Selectivity Predictions in a Non-Natural Biocatalytic Reaction.
Article in Journal of the American Chemical Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Data-driven construction of an imine reductase library capable of broad-scope reductive amination at equimolar substrate concentrations.Nature communications · 2026Article
- Molecular Origins of Simultaneous Chemo-, Enantio-, and Substrate Selectivity in Non-Natural Photoenzymatic Radical Reactions.Journal of the American Chemical Society · 2025Article
- Considerations in Pursuing Reaction Scope Generality.Angewandte Chemie (International ed. in English) · 2025Review
- Studying Noncovalent Interactions in Molecular Systems with Machine Learning.Chemical reviews · 2025Review
- Physics-based modeling in the new era of enzyme engineering.Nature computational science · 2025Review
- Directed Evolution of a Modular Polyketide Synthase Thioesterase for Generation of a Hybrid Macrocyclic Ring System.ACS catalysis · 2025Article
- Article
- Supramolecular Catalyzed Cascade Reduction of Azaarenes Interrogated via Data Science.Journal of the American Chemical Society · 2024Article
- Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity.Nature communications · 2024Article
- Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity.ACS catalysis · 2024Article
- Asymmetric Synthesis of α-Chloroamides via Photoenzymatic Hydroalkylation of Olefins.Journal of the American Chemical Society · 2024Article
- Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.ACS central science · 2024Review
- Novel applications of photobiocatalysts in chemical transformations.RSC advances · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The study of non-natural biocatalytic transformations relies heavily on empirical methods, such as directed evolution, for identifying improved variants. Although exceptionally effective, this approach provides limited insight into the molecular mechanisms behind the transformations and necessitates multiple protein engineering campaigns for new reactants. To address this limitation, we disclose a strategy to explore the biocatalytic reaction space and garner insight into the molecular mechanisms driving enzymatic transformations. Specifically, we explored the selectivity of an "ene"-reductase, GluER-T36A, to create a data-driven toolset that explores reaction space and rationalizes the observed and predicted selectivities of substrate/mutant combinations. The resultant statistical models related structural features of the enzyme and substrate to selectivity and were used to effectively predict selectivity in reactions with out-of-sample substrates and mutants. Our approach provided a deeper understanding of enantioinduction by GluER-T36A and holds the potential to enhance the virtual screening of enzyme mutants.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.