Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
WILDkCAT: extract, retrieve, and predict enzyme turnover numbers of constraint-based metabolic models. |
| Συγγραφείς: |
Escoffier, Hugues1 (AUTHOR), Linster, Carole L2 (AUTHOR), Sauter, Thomas1 (AUTHOR) |
| Πηγή: |
Bioinformatics. Aug2026, Vol. 42 Issue 8, p1-5. 5p. |
| Θεματικοί όροι: |
*Enzyme kinetics, *Metabolic models, *Bioinformatics, *Escherichia coli, *Chemical species, *Python programming language |
| Περίληψη: |
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, k cat 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 k cat 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 k cat 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. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: |
Academic Search Index |