wildkcat 0.0.2

Extract, Retrieve and Predict kcat values for a metabolic model to run enzyme constrained metabolic pipelines.

WILDkCAT

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

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Installation

1. Clone the repository:

git clone https://github.com/h-escoffier/WILDkCAT.git
cd WILDkCAT

2. Install Miniconda or Anaconda.

3. Create and activate the environment:

conda env create -f environment.yml
conda activate wildkcat-env

4. 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=[e-mail]
BRENDA_EMAIL=[e-mail]
BRENDA_PASSWORD=your_password

• Replace the placeholders with the credentials from the account you created on the BRENDA website.
• Make sure this file is not shared publicly (e.g., add .env to your .gitignore) since it contains sensitive information.
• The scripts will automatically read these environment variables to authenticate and retrieve kcat values.

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Scripts Overview

extract_kcat.py

• 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.

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retrieve_kcat.py

• If multiple enzymes are provided, searches UniProt for catalytic activity.
• If multiple catalytic enzymes are identified, store all.
• When multiple enzymes are 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 lowest kcat value

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predict_kcat.py

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