EnzymePynetics is a Python-based tool designed for fitting time-course data of enzyme-catalyzed reactions to various kinetic models.
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Estimator Initialization: Utilizes an
Estimatorobject for each dataset, derived from an EnzymeMLDocument. This document is created by MTPHandler and includes vital information on measurement data and reaction components. - Reaction Equation Definition: Allows for the specification of educts, products, and enzymes in the reaction equation. Users can define different substrate and enzyme rate laws.
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Kinetic Parameter Initialization: Includes parameters like turnover number (
$k_{cat}$ ), Michaelis constant ($K_M$ ), competitive inhibition constant ($K_{ic}$ ), uncompetitive inhibition constant ($K_{iu}$ ), and time-dependent enzyme inactivation rate ($k_{ie}$ ). These parameters are initialized with specific values and bounds based on the dataset. -
Parameter Estimation: Features a robust fitting process using substrate concentration data, integrating the rate laws of a
ReactionSystem. Utilizes the Lmfit implementation of the Levenberg-Marquardt algorithm for fitting. - Comprehensive Analysis and Visualization: After fitting, it provides an overview of parameters, their standard errors, and the Akaike Information Criterion (AIC) for each system. Additionally, a correlation matrix and interactive visualizations of fitted systems are available for evaluating the fit quality.
- Integration with EnzymeMLDocument: Selected kinetic models, along with estimated parameters and uncertainties, are added to the EnzymeMLDocument. This document is then serialized as an SBML-compliant .omex archive for each experimental condition.
To begin using EnzymePynetics, clone the repository:
git clone https://github.com/FAIRChemistry/EnzymePynetics/