EnzymeHunter

Overview

EnzymeHunter: Achieving fine-grained enzyme function prediction with a hierarchically-aware contrastive learning framework. This source code was developed and tested on Linux (CentOS) with Python 3.9.

EnzymeHunter Architecture

Architecture and workflow of the EnzymeHunter model：

Installation & Setup

1. Clone the repository

   git clone https://github.com/cgxbio/EnzymeHunter.git
   cd EnzymeHunter
   

2. Create and activate the virtual environment

   conda env create -f environment.yml
   conda activate EnzymeHunter
   

Data and Model Download

Please download the pretrained data and model files from Zenodo:

After downloading, place the contents into the following directories:

EnzymeHunter/
├── data/          # Place dataset files here
│   └── pdb/       # (PDB mode) Place PDB files here, named {UNIPROT_ID}.pdb
├── model/         # Place model files here
├── EnzymeHunter.py
├── ...

Usage Example

Here are the common examples of using the EnzymeHunter, corresponding to different running modes:

1. Normal Mode (Default Mode)

Use this mode when you need the program to automatically distinguish between enzymes and non-enzymes, and only predict EC numbers for proteins classified as enzymes:

# Example: run prediction on a dataset where not all proteins are enzymes
python EnzymeHunter.py --dataset example_test --all_are_enzymes False

Explanation:

• --example_test is your test dataset name (without the .fasta extension)
• --The program will first perform enzyme/non-enzyme classification prediction
• --Only proteins predicted as enzymes (pred_label=1) will undergo subsequent EC number prediction
• --Non-enzyme proteins will be marked with EC number 0.0.0.0

2. All-Enzymes Mode

Use this mode when you know all proteins in the dataset are enzymes and want to skip the enzyme/non-enzyme classification step, proceeding directly to EC number prediction:

# Example: run prediction assuming all proteins are enzymes
python EnzymeHunter.py --dataset example_test --all_are_enzymes True

Explanation:

• --example_test is your enzyme dataset name (without the .fasta extension)
• --The --all_are_enzymes flag forces the program to treat all proteins as enzymes
• --Skips the enzyme/non-enzyme classification step and proceeds directly to EC number prediction
• --All proteins will have pred_label set to 1 and pred_prob set to 1.0

3. PDB-Based Contact Map Mode

By default, EnzymeHunter uses ESM2-predicted contact maps. You can switch to PDB-based contact maps (computed from AlphaFold or ESMFold 3D structures) by setting --contact_map_source pdb:

1. Place your PDB structure files (named {UNIPROT_ID}.pdb) into ./data/pdb/
2. Run the pipeline with --contact_map_source pdb:

In this mode, EnzymeHunter will use the model trained with PDB-derived contact maps.

# Normal mode with PDB contact maps
python EnzymeHunter.py --dataset example_test --all_are_enzymes False --contact_map_source pdb

# All-enzymes mode with PDB contact maps
python EnzymeHunter.py --dataset example_test --all_are_enzymes True --contact_map_source pdb

Output

After running, the prediction results will be saved to:

./results/example_test_final_pred_results.csv