ProtSSSD

• Introduction
• Installation
• Usage
• Contributing
• License
• Acknowledgements

Introduction

ProtSSSD is a powerful model that leverages a large protein language model (ESM) to predict protein structural properties, including disorder. This model outperforms other single-sequence methods, providing more accurate and reliable predictions for various protein characteristics.

Installation

1. Clone the Repository

git clone https://github.com/ISYSLAB-HUST/ProtSSSD.git
cd ProtSSSD

2. Create and Activate Environment

conda env create -f environment.yml
conda activate protsssd

Usage

CAID-2 Disorder

python predict_disorder.py ./dataset/caid2/ ./weight/ProtSSSD.pth --output result_disorder.json

Issues

If you encounter any problems, please open an issue.

Pull Requests

1. Fork the repository
2. Create a new branch (git checkout -b feature/yourfeature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature/yourfeature)
5. Create a new Pull Request

License

This project is licensed under the MIT License for the code and a custom license for the parameter files.

Code License

The code in this project is licensed under the MIT License. See the LICENSE file for more details.

Parameter Files License

The parameter files in this project are licensed under a custom license. Educational use is free of charge, while commercial use requires a commercial license. See the PARAMETER_LICENSE file for more details.

Acknowledgements

ProtSSSD with and/or references the following separate libraries and packages:

• PyTorch
• biopython
• einops
• torchmetrics
• esm
• minLoRA