MechFind: A Computational Framework for De Novo Prediction of Enzyme Mechanisms

About the Project

MechFind addresses the "mechanism gap" in bioinformatics by providing a high-throughput tool to generate plausible mechanistic hypotheses. It operates in two main stages:

1. Parsimony Search: Identifies the top-ten most parsimonious (fewest steps) mechanisms using a Mixed-Integer Linear Programming (MILP) formulation.
2. Similarity Re-ranking: Re-ranks these ten candidates based on their mechanistic similarity to a curated database of known enzyme mechanisms from the M-CSA.

This repository provides a self-contained example to demonstrate the workflow on a sample reaction.

Getting Started

To run the example, you will need a Python environment with several scientific computing packages installed.

Prerequisites

• Python 3.8+
• Jupyter Notebook or JupyterLab
• The following Python packages:
  • pulp (for MILP)
  • pandas
  • rdkit-pypi (for cheminformatics)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/MechFind.git
   cd MechFind

2. Create a requirements.txt file with the following content:

   pulp
   pandas
   rdkit-pypi
   

3. Install the required packages:

   pip install -r requirements.txt

How to Run the Example

The primary way to use this repository is through the provided Jupyter Notebook.

1. Launch Jupyter: Open your terminal, navigate to the MechFind directory, and run:

   jupyter notebook

   or

   jupyter lab

2. Open the Notebook: In the Jupyter interface that opens in your browser, click on MechFind_example.ipynb.

3. Run the Code: Execute the cells in the notebook from top to bottom. The notebook is structured to:

   • Import necessary libraries.
   • Define all helper functions.
   • Load and pre-process the required data files.
   • Define the main MechFind function.
   • Run an example prediction and display the results.

Understanding the Files

• MechFind_example.ipynb: The main file containing all the code. It defines the core MechFind function, its helpers, loads the necessary data, and runs a demonstration.
• Unique_Rules.csv: The elementary rules matrix. This crucial data file contains the moiety changes for each of the 4,143 unique elementary reaction rules derived from the M-CSA database.
• M-CSA_arrow_rules_r0.json: Pre-processed "arrow environment" data from the M-CSA. This file contains the fundamental chemical transformations that are used to calculate the similarity score for re-ranking mechanisms.

Understanding the Output

The example in the notebook produces two main outputs:

1. solutions (List of Mechanisms): This is a list containing the top 10 predicted mechanisms, sorted by their similarity score (best first). Each mechanism is itself a list of rule IDs that, when combined, produce the overall reaction.

2. Mechanism_Matrix (DataFrame): This function takes one of the predicted mechanisms (e.g., solutions[0]) and visualizes it as a table. The DataFrame shows the net change of each moiety for the overall reaction (RXN), the counts in the reactants and products, and the specific changes contributed by each elementary rule in the predicted mechanism.