Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Alan M. Luu, Jacob R. Leistico, Tim Miller, Somang Kim, Jun S. Song

Research output: Contribution to journalArticlepeer-review


Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.
Original languageEnglish (US)
Article number572
Pages (from-to)NA
Issue number4
StatePublished - Apr 2021


  • Deep learning
  • Epitope binding specificity
  • Metric learning
  • Multimodal learning
  • T cell receptors

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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