TRAP: A robust deep learning workflow to predict CD8+ T-cell epitopes

TRAP allows for the robust prediction of CD8+ T-cell epitopes from MHC-I ligands. It employs a deep learning-based platform for predicting immunogenicity and a DecisionTree classifier to estimate the degree of correctness.

This GitHub repository includes documentation for:

• TRAP-CNN model structure (model.py)
• RSAT (RSAT.R)

Installation

git clone https://github.com/ChloeHJ/TRAP.git

We highly recommend you to create conda environment with python version >= 3.9.

conda create -n trap python=3.9
conda activate trap
pip install -r requirements.txt

Dash web application

The dash web application can be found here: http://35.176.114.129:8050/

Data

All required data, models and supplementary notebooks are stored in: https://drive.google.com/drive/folders/15A2P5xP2c-q48vVGPRB7h7uHEMycPYoX?usp=drive_link

We provide gbm_example_test_data.csv as an example test data. The input data should have the same structure as the example dataset, and contain peptides having 9-10 amino acids in length. Minimal requirments for the input data are Peptide sequence and -log2(NetMHCpan rank score). Please select the pathogenic or self-antigen (cancer, autoantigens, etc.) model and insert your list of peptides on the application. The model may take 5-10 minutes to generate the output. We highly recommend the users to have <100 test peptides at a time due to limited server space. The "Tutorial for TRAP web application" document contains a detailed tutorial on how to use the web application.

Interpretation of the result:

• TRAP: Immunogenicity (TRAP > 0.5: Positive, TRAP < 0.5: Negative)
• MCDropout: MaxProb value from averaged 100 Monte Carlo dropouts
• Confidence: Degree of correctness predicted using MCDropout (TRAP > ~0.75: High confidence)

The data/ includes training data for these modules:

• pathogenic_db.csv and selfantigen_data_forMODEL.csv are the training datasets for TRAP.
• ood_dropout_pathogenic.csv and ood_dropout_selfantigen.csv are training data for OOD MCDropout-based OOD classifier.
• Autoantigen_5data.csv are autoantigen and cancer-associated antigens gathered from 5 databases.
• hsProtemeDF.RData is reference human proteome sequence for RSAT calculation.
• Autoantigen_peptide_vector.RData and iedb_dissimilar_proteome_blosum62.RData are processed files from RSAT calculations.

Citation

Please cite TRAP as following: Lee, C. H., Huh, J., Buckley, P. R., Jang, M., Pinho, M. P., Fernandes, R. A., Antanaviciute, A., Simmons, A., & Koohy, H. (2023). A robust deep learning workflow to predict CD8 + T-cell epitopes. Genome Medicine, 15(1), 70. https://doi.org/10.1186/s13073-023-01225-z

Available from: https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-023-01225-z

You could also use the BibTeX form below.

@article{lee2023robust,
  title={A robust deep learning workflow to predict CD8+ T-cell epitopes},
  author={Lee, Chloe H and Huh, Jaesung and Buckley, Paul R and Jang, Myeongjun and Pinho, Mariana Pereira and Fernandes, Ricardo A and Antanaviciute, Agne and Simmons, Alison and Koohy, Hashem},
  journal={Genome Medicine},
  volume={15},
  number={1},
  pages={70},
  year={2023},
  publisher={Springer}
}

License

Shield:

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.