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

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

JOURNAL=Frontiers in Immunology

VOLUME=11

YEAR=2020

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2020.01803

DOI=10.3389/fimmu.2020.01803

ISSN=1664-3224

ABSTRACT=<p>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 <ext-link ext-link-type="uri" xlink:href="https://github.com/louzounlab/ERGO" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/louzounlab/ERGO</ext-link>. ERGO is also available through a webserver at: <ext-link ext-link-type="uri" xlink:href="http://tcr.cs.biu.ac.il/" xmlns:xlink="http://www.w3.org/1999/xlink">http://tcr.cs.biu.ac.il/</ext-link>.</p>