NRPSTransformer

A Transformer-Based Predictor for Nonribosomal Peptide Synthetases (NRPS) Specificity

Welcome! You can try our web demo here: http://www.nrpstransformer.cn

Overview

NRPSTransformer is a deep learning model designed to predict the substrate specificity of adenylation (A) domains in Nonribosomal Peptide Synthetases (NRPS). It employs a transformer-based architecture that capitalizes on transfer learning. It generates high-quality protein embeddings from a large, pre-trained language model, which are subsequently fed into a fine-tuned classifier to achieve state-of-the-art prediction accuracy of 93%.

Prerequisites

Before you begin, ensure you have the following installed:

• Conda/Miniconda: To manage the project environment. You can find installation instructions here.
• Git LFS: For handling large model files. You can install it by following the instructions here.

Installation [Estimated Time: 10-20 min]

Please follow these steps carefully to set up the project environment and download the necessary models.

1. Clone the Repository First, clone this repository to your local machine.

git clone https://github.com/westlake-repl/NRPSTransformer.git
cd NRPSTransformer

2. Set Up the Conda Environment Create and activate a dedicated conda environment using the provided requirement.txt file.

# Create a new conda environment named 'NRPS' with Python 3.8.5
conda create -n NRPS python=3.8.5 -y
# Activate the newly created environment
conda activate NRPS
# Install the required Python packages
pip install -r requirement.txt

3. Download the ESM-2 Backbone Model This project uses the esm2_t33_650M_UR50D model as a backbone. You need Git LFS to clone it from Hugging Face.

# Initialize Git LFS
git lfs install
# Clone the ESM-2 model repository
git clone https://huggingface.co/facebook/esm2_t33_650M_UR50D

4. Download Project Checkpoints and Labels The model checkpoints and class labels are hosted on Zenodo. Download them and place them in the correct directories.

# Create the necessary directories
mkdir -p checkpoints
mkdir -p model/class_label

# Download checkpoint files into the 'checkpoints' directory
wget -O checkpoints/all.ckpt https://zenodo.org/records/15771488/files/all.ckpt
wget -O checkpoints/benchmark.ckpt https://zenodo.org/records/15771488/files/benchmark.ckpt
wget -O checkpoints/clade.ckpt https://zenodo.org/records/15771488/files/clade.ckpt

# Download label mapping files into the 'model/class_label' directory
wget -O model/class_label/labelid2lable-17.pt https://zenodo.org/records/15771488/files/labelid2lable-17.pt
wget -O model/class_label/labelid2label-43.pt https://zenodo.org/records/15771488/files/labelid2label-43.pt

After completing these steps, your project directory should look like this:

NRPSTransformer/
├── checkpoints/
│   ├── all.ckpt
│   ├── benchmark.ckpt
│   └── clade.ckpt
├── esm2_t33_650M_UR50D/
│   └── ... (Hugging Face model files)
├── model/
│   └── class_label/
│       ├── labelid2lable-17.pt
│       └── labelid2label-43.pt
├── requirement.txt
├── run.sh
└── ... (other project files)

Usage

The primary way to run predictions is through the run.sh script. It provides an interactive command-line interface to specify your input and output files.

1. Execute the Script Make sure you are in the NRPSTransformer root directory and the NRPS conda environment is active.

bash run.sh

2. Interactive Prompts The script will prompt you for two pieces of information:

• Input File Path: The path to your FASTA file containing the protein sequences of the A-domains you want to predict.
• Result File Path: The path where the prediction results will be saved in CSV format. You can either type a new path or press Enter to accept the default values shown in the parentheses.

Here is an example of the terminal interaction:

Please enter the input sequence file path (Default: ./sequence/sequence.fasta):
# User can type a path like './my_sequences.fasta' or just press Enter

setting input file to ./sequence/sequence.fasta

Please enter the result file path (Default: ./result/result.csv): 
# User can type a path like './my_results.csv' or just press Enter

setting result path to ./result/result.csv

# The prediction process will start now...
# ...
# ...
# Prediction finished. Results are saved in ./result/result.csv

The program will then run the prediction pipeline, and upon completion, a message will confirm that the results have been saved to your specified output file.