BERTrand-peptide: TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing.

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Abstract

The advent of T cell receptor (TCR) sequencing experiments allowed for a significant increase in the amount of peptide: TCR binding data available and a number of machine learning models appeared in recent years. High-quality prediction models for a fixed epitope sequence are feasible, provided enough known binding TCR sequences are available. However, their performance drops significantly for previously unseen peptides.We prepare the dataset of known peptide: TCR binders and augment it with negative decoys created using healthy donors’ T-cell repertoires. We employ deep learning methods commonly applied in Natural Language Processing (NLP) to train part a peptide: TCR binding model with a degree of cross-peptide generalization (0.69 AUROC). We demonstrate that BERTrand outperforms the published methods when evaluated on peptide sequences not used during model training.The datasets and the code for model training are available at https://github.com/SFGLab/bertrand.Supplementary data are available at Bioinformatics online.© The Author(s) 2023. Published by Oxford University Press.