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TCR clustering by contrastive learning on antigen specificity.

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.

SUBMITTER: Pertseva M

PROVIDER: S-EPMC11317525 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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TCR clustering by contrastive learning on antigen specificity.

Pertseva Margarita M Follonier Oceane O Scarcella Daniele D Reddy Sai T ST

Briefings in bioinformatics 20240701 5

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 ...[more]

PMID: 39129361

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