TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes

• 成果类型: Article
• 署名作者: Meynard-Piganeau, Barthelemy; Feinauer, Christoph; Weigt, Martin; Walczak, Aleksandra M.; Mora, Thierry
• 署名单位: Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite; Bocconi University; Centre National de la Recherche Scientifique (CNRS); Universite Paris Cite; Universite PSL; Ecole Normale Superieure (ENS); Sorbonne Universite
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10916
• DOI: 10.1073/pnas.2316401121
• 发表日期: 2024-06-11
• 关键词: specificity; antigen; repertoire; activation

摘要

The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a method, Transformer -based Unsupervised Language model for Interacting Peptides and T cell receptors (TULIP), that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state -of -the -art models and offers a promising direction for the development of more accurate TCR epitope recognition models.