  Computational prediction of the interaction of T cell receptors (TCRs) and
their ligands is a grand challenge in immunology. Despite advances in
high-throughput assays, specificity-labelled TCR data remains sparse. In other
domains, the pre-training of language models on unlabelled data has been
successfully used to address data bottlenecks. However, it is unclear how to
best pre-train protein language models for TCR specificity prediction. Here we
introduce a TCR language model called SCEPTR (Simple Contrastive Embedding of
the Primary sequence of T cell Receptors), capable of data-efficient transfer
learning. Through our model, we introduce a novel pre-training strategy
combining autocontrastive learning and masked-language modelling, which enables
SCEPTR to achieve its state-of-the-art performance. In contrast, existing
protein language models and a variant of SCEPTR pre-trained without
autocontrastive learning are outperformed by sequence alignment-based methods.
We anticipate that contrastive learning will be a useful paradigm to decode the
rules of TCR specificity.
