TCR clustering by contrastive learning on antigen specificity.

IF 6.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Briefings in bioinformatics Pub Date : 2024-07-25 DOI:10.1093/bib/bbae375

Margarita Pertseva, Oceane Follonier, Daniele Scarcella, Sai T Reddy

引用次数: 0

Abstract

Effective clustering of T-cell receptor (TCR) sequences could be used to predict their antigen-specificities. TCRs with highly dissimilar sequences can bind to the same antigen, thus making their clustering into a common antigen group a central challenge. Here, we develop TouCAN, a method that relies on contrastive learning and pretrained protein language models to perform TCR sequence clustering and antigen-specificity predictions. Following training, TouCAN demonstrates the ability to cluster highly dissimilar TCRs into common antigen groups. Additionally, TouCAN demonstrates TCR clustering performance and antigen-specificity predictions comparable to other leading methods in the field.

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通过对抗原特异性的对比学习进行 TCR 聚类。

T细胞受体（TCR）序列的有效聚类可用于预测其抗原特异性。具有高度不同序列的 TCR 可与相同的抗原结合，因此将它们聚类到一个共同的抗原组是一项核心挑战。在这里，我们开发了 TouCAN，这是一种依靠对比学习和预训练蛋白质语言模型来进行 TCR 序列聚类和抗原特异性预测的方法。经过训练后，TouCAN 展示了将高度不同的 TCR 聚类到共同抗原组中的能力。此外，TouCAN 的 TCR 聚类性能和抗原特异性预测能力可与该领域的其他领先方法相媲美。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Briefings in bioinformatics 生物-生化研究方法

CiteScore

13.20

自引率

13.70%

发文量

549

审稿时长

6 months

期刊介绍： Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.

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