ArticleProceedings of the National Academy of Sciences of the United States of America2016
Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers.
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Who cites it
127 citing papers in PubMed.
- EINN: An enzyme-informed neural network guided by an enzyme-constrained genome-scale metabolic model.Synthetic and systems biotechnology · 2027Article
- Katalyst: knowledge-guided semantic alignment via contrastive learning for enzyme turnover prediction.Bioinformatics (Oxford, England) · 2026Article
- WILDkCAT: extract, retrieve, and predict enzyme turnover numbers of constraint-based metabolic models.Bioinformatics (Oxford, England) · 2026Article
- Mitochondrial vulnerability to aldehydic load and the role mitochondrial aldehyde dehydrogenases.Pharmacological research · 2026Review
- An enzyme-specific protein language model for catalytic property prediction.Nature communications · 2026Article
- A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.Briefings in bioinformatics · 2026Review
- In Vivo Enzyme Catalytic Rates in Formate-Growing Methanococcus maripaludis.Microbial biotechnology · 2026Article
- Model-based inference of enzyme inhibitions from perturbation-induced metabolic dynamics.bioRxiv : the preprint server for biology · 2026Article
- EnzymeTuning improves enzyme-constrained metabolic modeling and proteome abundance prediction through deep learning.Nature communications · 2026Article
- Beyond Correlation: Constraint Architecture Explains Proteome-Metabolome Decoupling.International journal of molecular sciences · 2026Review
- Dissipatively Fueled Unidirectionally Communicating DNA Circuits That Control Biocatalysis.Angewandte Chemie (International ed. in English) · 2026Article
- The return of metabolism: biochemistry and physiology of glycolysis.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Accurate prediction of flux distributions compatible with metabolite concentration effects in genome-scale metabolic networks.PLoS computational biology · 2026Article
- Enhancing kCommunications biology · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Rubisco is slow across the tree of life.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- DeepMolecules: a web server for predicting enzyme and transporter-small molecule interactions.Nucleic acids research · 2025Article
- Dynamic Cytoplasm: A Physical Regulator of Enzymatic Function.Biochemistry · 2025Review
- Article
- Kinetic modules are sources of concentration robustness in biochemical networks.Science advances · 2025Article
67 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
Abstract
Turnover numbers, also known as kcat values, are fundamental properties of enzymes. However, kcat data are scarce and measured in vitro, thus may not faithfully represent the in vivo situation. A basic question that awaits elucidation is: how representative are kcat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate kmax(vivo), the observed maximal catalytic rate of an enzyme inside cells. Comparison with kcat values from Escherichia coli, yields a correlation ofr(2)= 0.62 in log scale (p < 10(-10)), with a root mean square difference of 0.54 (3.5-fold in linear scale), indicating that in vivo and in vitro maximal rates generally concur. By accounting for the degree of saturation of enzymes and the backward flux dictated by thermodynamics, we further refine the correspondence between kmax(vivo) and kcat values. The approach we present here characterizes the quantitative relationship between enzymatic catalysis in vitro and in vivo and offers a high-throughput method for extracting enzyme kinetic constants from omics data.
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