ArticlePLoS biology2021
Deep learning allows genome-scale prediction of Michaelis constants from structural features.
Article in PLoS biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- EINN: An enzyme-informed neural network guided by an enzyme-constrained genome-scale metabolic model.Synthetic and systems biotechnology · 2027Article
- Trade-off between flux efficiency and metabolic control shapes enzyme allocation.Molecular systems biology · 2026Article
- Predicting Enzyme Turnover Numbers and Enabling Rational Enzyme Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- High-throughput physics-based enzyme engineering.bioRxiv : the preprint server for biology · 2026Article
- Computational approach for identification and characterization of a glucose-tolerant thermostable β-glucosidase from marine metagenome.Molecular diversity · 2026Article
- EPIC: A Machine-Learning Framework for Product-Dependent Behavior in β‑Glucosidases.ACS omega · 2026Article
- Prioritising search for virtual screening via preliminary interpretable low-feature likelihood-based rankings of drug-target activity measures.BMC bioinformatics · 2026Article
- Metabolic reprogramming of myeloid cells in cancer: from lactate-NAMPT axis to AI-guided therapeutics.Experimental & molecular medicine · 2026Review
- Integrating Protein Language Models with Multimodal Embeddings to Accelerate Function Prediction of Uncharacterized Proteins.International journal of molecular sciences · 2026Review
- Graph-Aware AURALSTM: An Attentive Unified Representation Architecture with BiLSTM for Enhanced Molecular Property Prediction.Molecular diversity · 2026Article
- The return of metabolism: biochemistry and physiology of glycolysis.Biological reviews of the Cambridge Philosophical Society · 2026Review
- A standardized workflow for kinetic metabolic model curation and dissemination.PLoS computational biology · 2026Article
- KinForm: kinetics-informed feature optimised representation models for enzyme kNPJ systems biology and applications · 2026Article
- The Arabidopsis neutral amino acid transporter UmamiT20 confers Botrytis cinerea susceptibility.Journal of experimental botany · 2026Article
- Enzyme Catalytic Parameters and Evolution Across the Dissipation Plane.International journal of molecular sciences · 2026Review
- CACLENS: A Multitask Deep Learning System for Enzyme Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Discover the maze-like network for glabridin biosynthesis.Nature communications · 2026Article
- COBRA-k: A powerful framework bridging constraint-based and kinetic metabolic modeling.Science advances · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- GotEnzymes2: expanding coverage of enzyme kinetics and thermal properties.Nucleic acids research · 2026Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Michaelis constant KM describes the affinity of an enzyme for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme-substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts KM values for natural enzyme-substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and on a deep numerical representation of the enzyme's amino acid sequence. We provide genome-scale KM predictions for 47 model organisms, which can be used to approximately relate metabolite concentrations to cellular physiology and to aid in the parameterization of kinetic models of cellular metabolism.
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