ProteiNN is a Transformer network trained to predict end-to-end single-sequence protein structure by amino acid sequences. To find out more, check out the provided research paper:
- "Deep Learning for Protein Structure Prediction: Advancements in Structural Bioinformatics" (DOI: 10.1101/2023.04.26.538026)
- Also contained in the "PaperAndPresentation" folder is the research paper.
Run main.py to choose from either "train", "predict", or "metrics" modes; train will retrain the model and predict will provide users the option to enter an amino acid sequence to predict the structure of (which will be output as a PDB file). Metrics mode will load the trained model and report its classification metrics (e.g., precision, recall, F1 score, etc.) on the test dataset.
NOTE: if you have any issues with the code not working as is, it may be due to the version of the sidechainnet_casp12_30.pkl file that the sidechainnet library downloads. Please contact me if you would like a copy of the version that I used to train the model.
Bugs are tracked using the GitHub Issue Tracker.
Please use the issue tracker for the following purpose:
- To raise a bug request; do include specific details and label it appropriately.
- To suggest any improvements in existing features.
- To suggest new features or structures or applications.
The code is licensed under Apache License 2.0.
If you use this code for your research, please cite this project:
@software{Szelogowski_ProteiNN-Structure-Predictor_2023,
author = {Szelogowski, Daniel},
doi = {10.1101/2023.04.26.538026},
month = {April},
title = {{ProteiNN-Structure-Predictor}},
license = {Apache-2.0},
url = {https://github.com/danielathome19/ProteiNN-Structure-Predictor},
version = {1.0.0},
year = {2023}
}