Contrastive learning of T cell receptor representations

• URL: http://arxiv.org/abs/2406.06397v2
• Date: Thu, 10 Oct 2024 10:32:44 GMT
• Title: Contrastive learning of T cell receptor representations
• Authors: Yuta Nagano, Andrew Pyo, Martina Milighetti, James Henderson, John Shawe-Taylor, Benny Chain, Andreas Tiffeau-Mayer,
• Abstract summary: We introduce a TCR language model called SCEPTR, capable of data-efficient transfer learning. We introduce a novel pre-training strategy combining autocontrastive learning and masked-language modelling. We anticipate that contrastive learning will be a useful paradigm to decode the rules of TCR specificity.
• Score: 11.053778245621544
• License:
• Abstract: 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.

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