Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs

Ido Springer, Hanan Besser, Nili Tickotsky-Moskovitz, Shirit Dvorkin, Yoram Louzoun

Research output: Contribution to journalArticlepeer-review

78 Scopus citations

Abstract

Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. We employ new Natural Language Processing (NLP) based methods to predict whether any TCR and peptide bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO reaches similar results to state of the art methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO. ERGO is also available through a webserver at: http://tcr.cs.biu.ac.il/.

Original languageEnglish
Article number1803
JournalFrontiers in Immunology
Volume11
DOIs
StatePublished - 25 Aug 2020

Bibliographical note

Publisher Copyright:
© Copyright © 2020 Springer, Besser, Tickotsky-Moskovitz, Dvorkin and Louzoun.

Keywords

  • TCR repertoire analysis
  • autoencoder (AE)
  • deep learning
  • epitope specificity
  • evaluation methods
  • long short-term memory (LSTM)
  • machine learning

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