Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction

Ido Springer, Nili Tickotsky, Yoram Louzoun

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

Introduction: Predicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This affinity may be affected by different components of the TCR, the peptide, and the MHC allele. Historically, the main element used in TCR-peptide binding prediction was the Complementarity Determining Region 3 (CDR3) of the beta chain. However, recently the contribution of other components, such as the alpha chain and the other V gene CDRs has been suggested. We use a highly accurate novel deep learning-based TCR-peptide binding predictor to assess the contribution of each component to the binding. Methods: We have previously developed ERGO-I (pEptide tcR matchinG predictiOn), a sequence-based T-cell receptor (TCR)-peptide binding predictor that employs natural language processing (NLP) -based methods. We improved it to create ERGO-II by adding the CDR3 alpha segment, the MHC typing, V and J genes, and T cell type (CD4+ or CD8+) as to the predictor. We then estimate the contribution of each component to the prediction. Results and Discussion: ERGO-II provides for the first time high accuracy prediction of TCR-peptide for previously unseen peptides. For most tested peptides and all measures of binding prediction accuracy, the main contribution was from the beta chain CDR3 sequence, followed by the beta chain V and J and the alpha chain, in that order. The MHC allele was the least contributing component. ERGO-II is accessible as a webserver at http://tcr2.cs.biu.ac.il/ and as a standalone code at https://github.com/IdoSpringer/ERGO-II.

Original languageEnglish
Article number664514
JournalFrontiers in Immunology
Volume12
DOIs
StatePublished - 26 Apr 2021

Bibliographical note

Publisher Copyright:
© Copyright © 2021 Springer, Tickotsky and Louzoun.

Keywords

  • TCR - T cell receptor
  • TCR repertoire analysis
  • autoencoder (AE)
  • deep learning
  • epitope specificity
  • long short-term memory (LSTM)
  • machine learning
  • peptide binding

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