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

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.

Guardado en:

Detalles Bibliográficos
Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/0fd24268ab1c4d9e95f4d96aaa1b710a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0fd24268ab1c4d9e95f4d96aaa1b710a
record_format	dspace
spelling	oai:doaj.org-article:0fd24268ab1c4d9e95f4d96aaa1b710a2021-12-02T19:13:48ZNetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data10.1038/s42003-021-02610-32399-3642https://doaj.org/article/0fd24268ab1c4d9e95f4d96aaa1b710a2021-09-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-02610-3https://doaj.org/toc/2399-3642Montemurro 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.Alessandro MontemurroViktoria SchusterHelle Rus PovlsenAmalie Kai BentzenVanessa JurtzWilliam D. ChronisterAustin CrinklawSine R. HadrupOle WintherBjoern PetersLeon Eyrich JessenMorten NielsenNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 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 NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data
description	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.
format	article
author	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
author_facet	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
author_sort	Alessandro Montemurro
title	NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data
title_short	NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data
title_full	NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data
title_fullStr	NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data
title_full_unstemmed	NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data
title_sort	nettcr-2.0 enables accurate prediction of tcr-peptide binding by using paired tcrα and β sequence data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0fd24268ab1c4d9e95f4d96aaa1b710a
work_keys_str_mv	AT alessandromontemurro nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata AT viktoriaschuster nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata AT helleruspovlsen nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata AT amaliekaibentzen nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata AT vanessajurtz nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata AT williamdchronister nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata AT austincrinklaw nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata AT sinerhadrup nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata AT olewinther nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata AT bjoernpeters nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata AT leoneyrichjessen nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata AT mortennielsen nettcr20enablesaccuratepredictionoftcrpeptidebindingbyusingpairedtcraandbsequencedata
_version_	1718377010793807872

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

Ejemplares similares