TSpred

This is the code for the paper "TSpred: a robust prediction framework for TCR-epitope interactions based on an ensemble deep learning approach using paired chain TCR sequence data" (BioRxiv preprint link: https://www.biorxiv.org/content/10.1101/2023.12.04.570002v2).  

Datasets

The datasets used in the paper can be obtained by uncompressing the datasets.zip file.

• NetTCR_full
• IMMREP
• NetTCR_bal
• NetTCR_strict
  • This dataset consists of 5 different cross validation datasets generated with 5 different random seeds.  

Setting up a conda environment for TSpred

First create a conda environment, and activate the environment:

conda create -n tspred python=3.7.0

conda activate tspred

Clone this repository:

git clone https://github.com/ha01994/TSpred.git
cd TSpred

Then install the package requirements using pip:

pip install -r requirements.txt

Then you're ready to go.  

Reproducing training/testing with TSpred (example_run)

Example training, validation, and test data for reproduction can be found in the example_run/data folder. Here is the how the format of the files in example_run/data should look like:

peptide,A1,A2,A3,B1,B2,B3,binder
NLVPMVATV,SVFSS,VVTGGEV,AGPEGGYSGAGSYQLT,SGDLS,YYNGEE,ASSVSGATADTQY,0
LLWNGPMAV,TRDTTYY,RNSFDEQN,ALSGEGTGRRALT,GTSNPN,SVGIG,AWSVQGTDTQY,0
TTDPSFLGRY,TSGFNG,NVLDGL,AVRVFNARLM,SNHLY,FYNNEI,ASSEEIAKNIQY,1

1) Data preprocessing

To change the data format to fit our data processing pipeline, cd example_run and run:

python get_data_ready.py

Running this code will produce example_run/formatted_data/data.csv. The file will look like:

pep_id,tcr_id,label,split
pep12,tcr254,0,train    
pep16,tcr3719,0,val
pep3,tcr2713,1,train

The sequences corresponding to pep_id's and tcr_id's can be found in example_run/formatted_data/ids_pep.csv and example_run/formatted_data/ids_tcr.csv.

Then you need to perform preprocessing on the formatted data, by running:

python pp.py

This will generate preprocessed files (in .npy and .pkl format) in the example_run/features folder. These are the files that are used for training and testing.

2) Training and testing

To train the model, run:

python train.py

The trained models will be saved in the folder example_run/save_dir/, and the training and validation losses will be saved in the folder example_run/losses/.

After training is finished, test the model by running:

python test.py

This code will load the saved models, evaluate the models on the test dataset, and return the ROC-AUC and PR-AUC performances of the model.

It will also generate example_run/predictions.csv, which provides information on peptide-TCR pairs along with the labels and the model predictions. The file format will look like:

pep_id,tcr_id,pep_seq,tcr_seq,label,prediction
pep10,tcr1445,SPRWYFYYL,TSESDYY_QEAYKQQN_AYFREGKLT_MNHEY_SMNVEV_ASSALTSAKRYEQF,1,0.5357
pep12,tcr5238,NLVPMVATV,TSGFYG_NALDGL_AVRDQEGNTPLV_MDHEN_SYDVKM_ASMGGSNEQF,1,0.5253
pep8,tcr4463,GLCTLVAML,TSGFNG_NVLDGL_AVRDSDYKLS_MNHNS_SASEGT_AAGDGDQETQY,0,0.0001

Use pre-trained TSpred model to make predictions on a test dataset (example_run2)

We also provide TSpred model pre-trained on all of the NetTCR_full dataset, so that you can make predictions on your own test dataset.

First cd example_run2. example_run2/test_data_sample.csv is provided as an example for an unseen epitope dataset. If you want to test on your own data, provide test data in the same format as example_run2/test_data_sample.csv.

Then run:

python predict.py

which will use the TSpred_CNN and TSpred_ensemble model weights in the example_run2/best_models folder to make predictions (ensemble model predictions).

The above code will return predictions in the ensemble_predictions.csv file. A score of 0 means that a pair is predicted to be non-binding and 1 means that a pair is predicted to bind. Therefore, the higher the score, the more likely a given TCR-peptide pair will bind, according to our model.

Contact information

If you have questions, please contact us at [email protected].