TCR clustering by contrastive learning on antigen specificity

• Published in: Briefings in bioinformatics Vol. 25; no. 5
• Main Authors: Pertseva, Margarita, Follonier, Oceane, Scarcella, Daniele, Reddy, Sai T
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 11.08.2024; Oxford Publishing Limited (England)
• Subjects: Amino acid sequence; Antigens; Clustering; Learning; Lymphocytes T; Performance prediction; Problem Solving Protocol; T cell receptors; contrastive learning; deep learning; ESM model; T cell receptor; TCR clustering
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbae375

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.