NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data

Alessandro Montemurro; Viktoria Schuster; Helle Rus Povlsen; Amalie Kai Bentzen; Vanessa Jurtz; William D. Chronister; Austin Crinklaw; Sine R. Hadrup; Ole Winther; Bjoern Peters; Leon Eyrich Jessen; Morten Nielsen

doi:10.1038/s42003-021-02610-3

Communications Biology (Sep 2021)

NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data

Alessandro Montemurro,
Viktoria Schuster,
Helle Rus Povlsen,
Amalie Kai Bentzen,
Vanessa Jurtz,
William D. Chronister,
Austin Crinklaw,
Sine R. Hadrup,
Ole Winther,
Bjoern Peters,
Leon Eyrich Jessen,
Morten Nielsen

Affiliations

Alessandro Montemurro: Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs
Viktoria Schuster: Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs
Helle Rus Povlsen: Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs
Amalie Kai Bentzen: Department of Health Technology, Section for Experimental and Translational Immunology, Technical University of Denmark, DTU, 2800 Kgs
Vanessa Jurtz: Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs
William D. Chronister: Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology
Austin Crinklaw: Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology
Sine R. Hadrup: Department of Health Technology, Section for Experimental and Translational Immunology, Technical University of Denmark, DTU, 2800 Kgs
Ole Winther: Department of Biology, Bioinformatics Centre, University of Copenhagen
Bjoern Peters: Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology
Leon Eyrich Jessen: Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs
Morten Nielsen: Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs

DOI: https://doi.org/10.1038/s42003-021-02610-3
Journal volume & issue: Vol. 4, no. 1
pp. 1 – 13

Abstract

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Montemurro et al. present NetTCR-2.0, a convolutional neural network-based tool for predicting the interactions between T cell receptors and MHC-peptide complexes. This tool demonstrates that the best predictions are made when CDR3 α or CDR3 β binding data are used in combination.

Published in Communications Biology

ISSN: 2399-3642 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General)
Website: https://www.nature.com/commsbio/

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