WILDkCAT
WILDkCAT is a set of scripts designed to extract, retrieve, and predict enzyme turnover numbers (kcat) for genome-scale metabolic models.
WILDkCAT produces a .tsv file with the retrieved and predicted kcat values for each combination of enzyme, substrates in your genome-scale metabolic model.
Each step of the pipeline also generates an HTML report that provides detailed information about the retrieval process to facilitate transparency and reproducibility.
The HTML reports are generated automatically after each stage of the workflow (extraction, retrieval, prediction) and can be opened directly in any web browser.
Installation
Install WILDkCAT directly from PyPI:
pip install wildkcat
Environment Setup
Provide your BRENDA login credentials and Entrez API email adress to query the BRENDA enzyme database and NCBI database.
Create a file named .env in the root of your project with the following content:
ENTREZ_EMAIL=your_registered_email@example.com
BRENDA_EMAIL=your_registered_email@example.com
BRENDA_PASSWORD=your_password
Usage
WILDkCAT can be used as scripts or via the CLI.
Command-Line Interface (CLI)
After installation, you can use the WILDkCAT CLI:
wildkcat --help
Example Workflow:
# Extract kcat data
wildkcat extraction \
path/to/my_model.json \
path/to/folder_output
# Retrieve kcat values from databases
wildkcat retrieval \
path/to/folder_output
'Organism name' \
20 30 \ # Temperature range
6.5 8.5 \ # pH range
# Generate input for CataPro
wildkcat prediction-part1 \
path/to/folder_output
9 # Limit penalty score
# Integrate CataPro prediction
wildkcat prediction-part2 \
path/to/folder_output
prediction_output.csv \
9 # Limit penalty score
# Generate summary report
wildkcat report \
path/to/my_model.json \
path/to/folder_output
Programatic Access
from wildkcat import run_extraction, run_retrieval, run_prediction_part1, run_prediction_part2, generate_summary_report
Example: E. coli Core Model
A ready-to-run example is available here. It demonstrates a full extraction, retrieval, and prediction workflow on the E. coli core model.
Key scripts
extract_kcat.py
- Identify all enyme-reaction combination in the model
- Verify if EC numbers are valid (incomplete or transferred via KEGG)
- If multiple enzymes are provided, searches UniProt for catalytic activity.
retrieve_kcat.py
- If the same enzyme is not found, computes identity percentages relative to the identified catalytic enzyme.
- Applies Arrhenius correction to values within the appropriate pH range.
- For rows with multiple scores, selects:
- The best score
- The highest identity percentage
- The closest organism (if sequence is not available)
- The highest kcat value
predict_kcat.py
- Predict kcat values not retrieved in experimental databases using machine learning.
cf. Refer to the documentation for a more detailed explanation.
Feedback & Improvements
Contributions, suggestions, and feedback are very welcome! If you encounter any issues, have ideas for new features, or notice room for improvement, feel free to open an issue or submit a pull request.
Cite us
If you use WILDkCAT in your research, please cite:
Escoffier H, Linster CL, Sauter T. WILDkCAT: extract, retrieve, and predict enzyme turnover numbers of constraint-based metabolic models. Bioinformatics (2026). https://doi.org/10.1093/bioinformatics/btag510
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Metadata
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Release files for wildkcat 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| wildkcat-0.3.1.tar.gz | 96.0 kB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| wildkcat-0.3.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 145.9 kB
Release files / wildkcat-0.3.1.tar.gz
| Download URL | wildkcat-0.3.1.tar.gz |
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| Size | 96.0 kB |
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| Size | 49.9 kB |
| Tags | Python 3 |
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