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Updated: Jun 8, 2025
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Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
Published on: January 16, 2016
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ENKIE: a package for predicting enzyme kinetic parameter values and their uncertainties
Mattia G Gollub1, Thierry Backes1, Hans-Michael Kaltenbach1
1Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4056 Basel, Switzerland.
Bioinformatics (Oxford, England)
|November 4, 2024
Summary
The ENzyme KInetics Estimator (ENKIE) predicts enzyme kinetic parameters like KM and kcat using Bayesian models. This tool provides reliable uncertainty estimates, aiding metabolic modeling.
Area of Science:
- Biochemistry
- Systems Biology
- Computational Biology
Background:
- Accurate enzyme kinetics are crucial for metabolic modeling.
- Limited availability and reliability of experimental kinetic parameters hinder progress.
Purpose of the Study:
- To develop a computational tool for predicting enzyme kinetic parameters.
- To estimate the uncertainty associated with these predictions.
Main Methods:
- Utilized Bayesian Multilevel Models.
- Incorporated five categorical predictors.
- Achieved prediction performance comparable to deep learning methods.
Main Results:
- ENKIE accurately predicts Michaelis constant (KM) and catalytic constant (kcat) values.
- Provided calibrated uncertainty predictions for kinetic parameters.
- Identified key sources of uncertainty in predictions.
Conclusions:
- ENKIE simplifies the creation of priors for Bayesian kinetic models.
- Offers a reliable method for estimating enzyme kinetic parameters.
- Facilitates advancements in dynamic and enzyme cost modeling.
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