WILDkCAT is a set of scripts designed to extract, retrieve, and predict enzyme turnover numbers (kcat) for genome-scale metabolic models.

Install WILDkCAT directly from PyPI:

pip install wildkcat

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

WILDkCAT can be used as scripts or via the 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
    6  # Limit matching score 

# Integrate CataPro prediction
wildkcat prediction-part2 \
    path/to/folder_output
    prediction_output.csv \
    6  # Limit matching score

# Generate summary report
wildkcat report \
    path/to/my_model.json \
    path/to/folder_output

from wildkcat import run_extraction, run_retrieval, run_prediction_part1, run_prediction_part2, generate_summary_report

A ready-to-run example is available here. It demonstrates a full extraction, retrieval, and prediction workflow on the E. coli core model.

• Verifies whether the reaction EC number exists.
• Retains inputs where reaction-associated genes/enzymes are not supported by KEGG.
• Retains inputs where no enzymes are provided by the model.
• If multiple enzymes are provided, searches UniProt for catalytic activity.

• 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

• If multiple enzymes are provided, searches UniProt for catalytic activity.
• Skips entries missing KEGG compound IDs.

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.