POP-UP TCR: Prediction of Previously Unseen Paired TCR-pMHC

Motivation T lymphocytes (T-cells) major role in adaptive immunity drives efforts to elucidate the mechanisms behind T-cell epitope recognition. Results We analyzed solved structures of T-cell receptors (TCRs) and their cognate epitopes and used the data to train a set of machine learning models, POP-UP TCR, that predict the binding of any peptide to any TCR, including peptide and TCR sequences that were not included in the training set. We address biological issues that should be considered in the design of machine learning models for TCR-peptide binding and suggest that models trained only on beta chains give satisfactory predictions. Finally, we apply our models to large data set of TCR repertoires from COVID-19 patients and find that TCRs from patients in severe/critical condition have significantly lower scores for binding SARS-coV-2 epitopes compared to TCRs from moderate patients (p-value <0.001). Availability and Implementation POP-Up TCR is available at: Contact nilibrac{at}bgu.ac.il ### Competing Interest Statement The authors have declared no competing interest.