TCR (T-cell Receptor) is a complex protein structure that is part of the T-cell’s surface membrane. It consists of two chains—alpha and beta or gamma and delta, depending on the type of T-cell. TCRs are designed to recognize and bind to specific antigens. The binding of the TCR to its corresponding antigen triggers signaling pathways within the T-cell, leading to immune responses such as the activation, proliferation, and targeted attack on the recognized threat.


On TCR Binding Predictors Failing to Generalize to Unseen Peptides

Several recent studies investigate TCR-peptide/-pMHC binding prediction using machine learning or deep learning approaches. Many of these methods achieve impressive results on test sets, which include peptide sequences that are also included in the training set. In this work, we investigate how state of the-art deep learning models for TCR-peptide/-pMHC binding prediction generalize to unseen peptides. We create a dataset including positive samples from IEDB, VDJdb, McPAS-TCR, and the MIRA set, as well as negative samples from both randomization and 10X Genomics assays. We name this collection of samples TChard. We propose the hard split, a simple heuristic for training/test split, which ensures that test samples exclusively present peptides that do not belong to the training set. We investigate the effect of different training/test splitting techniques on the models’ test performance, as well as the effect of training and testing the models using mismatched negative samples generated randomly, in addition to the negative samples derived from assays. Our results show that modern deep learning methods fail to generalize to unseen peptides. We provide an explanation why this happens and verify our hypothesis on the TChard dataset. We then conclude that robust prediction of TCR recognition is still far for being solved.