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arXiv 2406.06397q-bio.BMcs.AIcs.LG
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T细胞受体表征的对比学习

Contrastive learning of T cell receptor representations

University College London（伦敦大学学院）；Princeton University（普林斯顿大学）

Yuta Nagano, Andrew Pyo, Martina Milighetti, James Henderson, John Shawe-Taylor, Benny Chain, Andreas Tiffeau-Mayer

备注：
25 pages, 23 figures; additional analyses and improvements to existing figures
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AI总结：

针对带特异性标记的TCR数据稀缺、蛋白质语言模型预训练方式不明的问题，提出结合自对比学习与掩码语言建模的SCEPTR模型，实现TCR特异性预测的最优性能，为解析TCR特异性规则提供新范式。

摘要：

T细胞受体（TCR）与其配体相互作用的计算预测是免疫学领域的一项重大挑战。尽管高通量检测技术取得了进展，但带有特异性标记的TCR数据仍然十分稀缺。在其他领域中，基于无标注数据预训练语言模型已被成功用于解决数据瓶颈问题。然而，目前尚不清楚如何最佳地预训练蛋白质语言模型以用于TCR特异性预测。本文提出了一种名为SCEPTR（T细胞受体一级序列简单对比嵌入）的TCR语言模型，该模型具备数据高效的迁移学习能力。通过该模型，我们提出了一种结合自对比学习与掩码语言建模的新型预训练策略，这一策略使SCEPTR能够取得当前最优的性能。相比之下，现有的蛋白质语言模型以及未采用自对比学习预训练的SCEPTR变体，其性能均不及基于序列比对的方法。我们预计，对比学习将成为解析TCR特异性规则的一种有效范式。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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