MHC refers to “Major Histocompatibility Complex.” The Major Histocompatibility Complex is a set of genes that encode cell surface proteins essential for the immune system’s ability to recognize and distinguish between self and non-self cells. These proteins play a crucial role in T-cell receptor (TCR) binding, particularly in the context of peptide presentation.

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On TCR Binding Predictors Failing to Generalize to Unseen Peptides

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.