ENKIE: A package for predicting enzyme kinetic parameter values and their uncertainties.

Abstract

Motivation: Relating 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.

Results: The 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: Code and Python package are available at https://gitlab.com/csb.ethz/enkie and https://pypi.org/project/enkie/.

Supplementary information: Supplementary data are available at Bioinformatics online.