ArticleNature communications2018
Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers.
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Who cites it
97 citing papers in PubMed.
- EINN: An enzyme-informed neural network guided by an enzyme-constrained genome-scale metabolic model.Synthetic and systems biotechnology · 2027Article
- Article
- Metal binding site alignment enables network-driven discovery of recurrent geometries across sequence-divergent proteins and drug off-targets.PLoS computational biology · 2026Article
- AUKAT: Conditional VAE-Driven Augmentation and Neural Modeling of Enzyme Turnover Numbers.Biomolecules · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- EnzymeTuning improves enzyme-constrained metabolic modeling and proteome abundance prediction through deep learning.Nature communications · 2026Article
- KinForm: kinetics-informed feature optimised representation models for enzyme kNPJ systems biology and applications · 2026Article
- A geometric deep learning framework for genome-wide prediction of enzyme turnover number.Genome biology · 2026Article
- UniKineG: Unified-Coordinate Geometric Graphs Enable Robust Enzyme Kinetic Prediction.International journal of molecular sciences · 2026Article
- Enhancing kCommunications biology · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- Dynamic Constrained Allocation Flux Balance Analysis (dCAFBA).Methods in molecular biology (Clifton, N.J.) · 2026Article
- KmPred: prediction of Michaelis constants (Km) using an integrative machine learning framework.Frontiers in artificial intelligence · 2026Article
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-DependentACS synthetic biology · 2025Article
- A compact model of Escherichia coli core and biosynthetic metabolism.PLoS computational biology · 2025Article
- A structure-oriented kinetics dataset of enzyme-substrate interactions.Scientific data · 2025Article
- DeepMolecules: a web server for predicting enzyme and transporter-small molecule interactions.Nucleic acids research · 2025Article
- NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling.Briefings in bioinformatics · 2025Article
37 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics.
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Registered trials
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