ArticleBioinformatics (Oxford, England)2024
ENKIE: a package for predicting enzyme kinetic parameter values and their uncertainties.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Model-based inference of enzyme inhibitions from perturbation-induced metabolic dynamics.bioRxiv : the preprint server for biology · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- 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
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- 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
- Non-canonical plant metabolism.Nature plants · 2025Review
- Metabolic modeling of host-microbe interactions.Computational and structural biotechnology journal · 2025Review
- A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.PLoS computational biology · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
motivationRelating metabolite and enzyme abundances to metabolic fluxes requires reaction kinetics, core elements of dynamic and enzyme cost models. However, kinetic parameters have been measured only for a fraction of all known enzymes, and the reliability of the available values is unknown.
resultsThe ENzyme KInetics Estimator (ENKIE) uses Bayesian Multilevel Models to predict value and uncertainty of KM and kcat parameters. Our models use five categorical predictors and achieve prediction performances comparable to deep learning approaches that use sequence and structure information. They provide calibrated uncertainty predictions and interpretable insights into the main sources of uncertainty. We expect our tool to simplify the construction of priors for Bayesian kinetic models of metabolism. AVAILABILITY AND IMPLEMENTATION: Code and Python package are available at https://gitlab.com/csb.ethz/enkie and https://pypi.org/project/enkie/.
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