ArticleJournal of the American Chemical Society2019
Machine Learning Identifies Chemical Characteristics That Promote Enzyme Catalysis.
Article in Journal of the American Chemical Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Substrate-Specific Differences in Catalytic Strategy and Activity across Ketol-Acid Reductoisomerase Variants.The journal of physical chemistry. B · 2026Article
- Unlocking Enzyme Discovery: Leveraging Multi-Omics, Machine Learning, and De Novo Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Physics-based modeling in the new era of enzyme engineering.Nature computational science · 2025Review
- Substrate Turnover Dynamics Guide Ketol-Acid Reductoisomerase Redesign for Increased Specific Activity.ACS catalysis · 2024Article
- Revisiting Textbook Azide-Clock Reactions: A "Propeller-Crawling" Mechanism Explains Differences in Rates.Journal of the American Chemical Society · 2024Article
- LPATH: A Semiautomated Python Tool for Clustering Molecular Pathways.Journal of chemical information and modeling · 2023Article
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
- LPATH: A semi-automated Python tool for clustering molecular pathways.bioRxiv : the preprint server for biology · 2023Article
- Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning.Journal of chemical information and modeling · 2023Article
- Data-driven enzyme engineering to identify function-enhancing enzymes.Protein engineering, design & selection : PEDS · 2023Review
- IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database.Journal of chemical information and modeling · 2022Article
- Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways.ACS physical chemistry Au · 2022Article
- Learning to Identify Physiological and Adventitious Metal-Binding Sites in the Three-Dimensional Structures of Proteins by Following the Hints of a Deep Neural Network.Journal of chemical information and modeling · 2022Review
- Method for Identifying Common Features in Reactive Trajectories of a Transition Path Sampling Ensemble.Journal of chemical theory and computation · 2022Article
- Bioprospecting of microbial enzymes: current trends in industry and healthcare.Applied microbiology and biotechnology · 2022Review
- High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering.Chemical communications (Cambridge, England) · 2022Review
- Article
- Machine learning for enzyme engineering, selection and design.Protein engineering, design & selection : PEDS · 2021Review
- New Trends in Bioremediation Technologies Toward Environment-Friendly Society: A Mini-Review.Frontiers in bioengineering and biotechnology · 2021Review
- Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiOScientific reports · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Despite tremendous progress in understanding and engineering enzymes, knowledge of how enzyme structures and their dynamics induce observed catalytic properties is incomplete, and capabilities to engineer enzymes fall far short of industrial needs. Here, we investigate the structural and dynamic drivers of enzyme catalysis for the rate-limiting step of the industrially important enzyme ketol-acid reductoisomerase (KARI) and identify a region of the conformational space of the bound enzyme-substrate complex that, when populated, leads to large increases in reactivity. We apply computational statistical mechanical methods that implement transition interface sampling to simulate the kinetics of the reaction and combine this with machine learning techniques from artificial intelligence to select features relevant to reactivity and to build predictive models for reactive trajectories. We find that conformational descriptors alone, without the need for dynamic ones, are sufficient to predict reactivity with greater than 85% accuracy (90% AUC). Key descriptors distinguishing reactive from almost-reactive trajectories quantify substrate conformation, substrate bond polarization, and metal coordination geometry and suggest their role in promoting substrate reactivity. Moreover, trajectories constrained to visit a region of the reactant well, separated from the rest by a simple hyperplane defined by ten conformational parameters, show increases in computed reactivity by many orders of magnitude. This study provides evidence for the existence of reactivity promoting regions within the conformational space of the enzyme-substrate complex and develops methodology for identifying and validating these particularly reactive regions of phase space. We suggest that identification of reactivity promoting regions and re-engineering enzymes to preferentially populate them may lead to significant rate enhancements.
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