WILDkCAT: Extract, retrieve, and predict enzyme turnover numbers of constraint-based metabolic models

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ID: 320721
2026
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Abstract
Abstract Summary Accurate enzyme turnover numbers are essential for building enzyme-constrained genome-scale metabolic models. However, collecting and curating these parameters remains a major bottleneck. Indeed, kcat values are scattered across multiple databases, reported under varying experimental conditions, and often missing for many enzymes. To address this challenge, we present WILDkCAT, a Python-based pipeline that enables the retrieval of kcat values from wild-type enzyme measured under user-specified pH and temperature ranges for a given metabolic model. The application to Escherichia coli (iML1515) and Homo sapiens (Human-GEM) models demonstrated the ability of WILDkCAT to retrieve substantial kcat coverage and its applicability across diverse genome-scale models. Availability and Implementation WILDkCAT is available at https://github.com/sysbiolux/WILDkCAT and from PyPI. WILDkCAT works on all major operating systems and computer architectures. The documentation is available at https://sysbiolux.github.io/WILDkCAT. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7168042727 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hugues Escoffier, Carole Linster, Thomas Sauter
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag510
URL
https://doi.org/10.1093/bioinformatics/btag510
Keywords Keywords not found

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