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This repository contains a Streamlit app that provides a user-friendly interface for TRILL (TRaining and Inference using the Language of Life), a sandbox for creative protein engineering and discovery. This app allows researchers to perform various operations related to protein engineering using pretrained models and user-provided input files.
- Upload protein sequences in FASTA, PDB, or CIF formats
- Run various TRILL operations, such as embedding, fine-tuning, generating, folding, and visualizing
- Customize TRILL run configurations, such as the number of GPUs, RNG seed, and model type
- Extend the app to support more command-line arguments and features
To use this app, first install the dependencies and then run the Streamlit app using the following command:
$ streamlit run trill_app.py
The app should open in your default web browser, allowing you to interact with the TRILL program.
- Python 3.8 or later
- Streamlit
- TRILL-Proteins
- PyG-Lib and related dependencies
- Clone this repository:
$ git clone https://github.com/your_username/trill-streamlit-app.git
- Change into the
trill-streamlit-app
directory:
$ cd trill-streamlit-app
- Install the dependencies:
$ conda create -n trill_env python=3.10
$ conda activate trill_env
$ pip install streamlit
$ pip install trill-proteins
$ pip install pyg-lib torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.13.0+cu117.html
- Run the app:
$ streamlit run trill_app.py
If you'd like to contribute to this project, please follow these steps:
- Fork the repository
- Create a new branch for your feature or bugfix (
git checkout -b my-feature-branch
) - Make changes and commit them to your branch
- Push your changes to your forked repository (
git push origin my-feature-branch
) - Create a new Pull Request for your changes
- Zachary Martinez - martinez-zacharya - zmartine@caltech.edu
This project is licensed under the MIT License - see the LICENSE.md file for details