LilianDenzler/TCR_Graphs

Immunocore TCR sequences are numbered. A sturctural model is then created using alphafold. These structures are put into graphs. GCNs are used to predict stability.

TCR Analysis and Stability Enhancing Mutations using GCNs

By combining LLMs for sequence embedding and graph represetations for structural representation, TCR complex stability is predicted and stability-enhancing mutations are predicted. Biochemical basis for predictions are extracted.

Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Contributing
5. License
6. Contact

About The Project

Scope of the project:

• open source datasets are cleaned and preprocessed,
• graph representation of proteins and protein complexes

Getting Started

Prerequisites

Installation

1. Clone the repo

   git clone [https://github.com/LilianDenzler/TCR_Graphs.git](https://github.com/LilianDenzler/TCR_Graphs.git)

2. create and activate environment

   conda env create --file=tcrgraphs.yaml
   conda activate tcrgraphs

Usage

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE.txt for more information.

Contact

Don't hesitate to reach out!

Lilian Denzler- [][https://linkedin.com/liliandenzler] - zcbtlm0@ucl.ac.uk