Footprints of antigen processing boost MHC class II natural ligand predictions

Autores
Barra, Carolina M.; Alvarez, Bruno; Paul, Sinu; Sette, Alessandro; Peters, Bjoern; Andreatta, Massimo; Buus, Søren; Nielsen, Morten
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
BACKGROUND: Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing. METHODS: We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets. RESULTS: We show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand. CONCLUSIONS: The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands and T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.
Fil: Barra, Carolina M.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Alvarez, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Paul, Sinu. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Buus, Søren. University Of Copenhagen, Faculty Of Health Sciences; Dinamarca
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Materia
ANTIGEN PROCESSING
BINDING PREDICTIONS
ELUTED LIGANDS
MACHINE LEARNING
MASS SPECTROMETRY
MHC-II
NEURAL NETWORKS
T CELL EPITOPE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/102765
Acceder
id CONICETDig_6fab8fc22cae2d21920192b2122784fb
oai_identifier_str oai:ri.conicet.gov.ar:11336/102765
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Footprints of antigen processing boost MHC class II natural ligand predictionsBarra, Carolina M.Alvarez, BrunoPaul, SinuSette, AlessandroPeters, BjoernAndreatta, MassimoBuus, SørenNielsen, MortenANTIGEN PROCESSINGBINDING PREDICTIONSELUTED LIGANDSMACHINE LEARNINGMASS SPECTROMETRYMHC-IINEURAL NETWORKST CELL EPITOPEhttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3BACKGROUND: Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing. METHODS: We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets. RESULTS: We show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand. CONCLUSIONS: The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands and T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.Fil: Barra, Carolina M.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Alvarez, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Paul, Sinu. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Buus, Søren. University Of Copenhagen, Faculty Of Health Sciences; DinamarcaFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaSpringer Nature2018-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/102765Barra, Carolina M.; Alvarez, Bruno; Paul, Sinu; Sette, Alessandro; Peters, Bjoern; et al.; Footprints of antigen processing boost MHC class II natural ligand predictions; Springer Nature; Genome Medicine; 10; 1; 11-20181756-994XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1186/s13073-018-0594-6info:eu-repo/semantics/altIdentifier/url/https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-018-0594-6info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:25:05Zoai:ri.conicet.gov.ar:11336/102765instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:25:05.309CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Footprints of antigen processing boost MHC class II natural ligand predictions
title Footprints of antigen processing boost MHC class II natural ligand predictions
spellingShingle Footprints of antigen processing boost MHC class II natural ligand predictions
Barra, Carolina M.
ANTIGEN PROCESSING
BINDING PREDICTIONS
ELUTED LIGANDS
MACHINE LEARNING
MASS SPECTROMETRY
MHC-II
NEURAL NETWORKS
T CELL EPITOPE
title_short Footprints of antigen processing boost MHC class II natural ligand predictions
title_full Footprints of antigen processing boost MHC class II natural ligand predictions
title_fullStr Footprints of antigen processing boost MHC class II natural ligand predictions
title_full_unstemmed Footprints of antigen processing boost MHC class II natural ligand predictions
title_sort Footprints of antigen processing boost MHC class II natural ligand predictions
dc.creator.none.fl_str_mv Barra, Carolina M.
Alvarez, Bruno
Paul, Sinu
Sette, Alessandro
Peters, Bjoern
Andreatta, Massimo
Buus, Søren
Nielsen, Morten
author Barra, Carolina M.
author_facet Barra, Carolina M.
Alvarez, Bruno
Paul, Sinu
Sette, Alessandro
Peters, Bjoern
Andreatta, Massimo
Buus, Søren
Nielsen, Morten
author_role author
author2 Alvarez, Bruno
Paul, Sinu
Sette, Alessandro
Peters, Bjoern
Andreatta, Massimo
Buus, Søren
Nielsen, Morten
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ANTIGEN PROCESSING
BINDING PREDICTIONS
ELUTED LIGANDS
MACHINE LEARNING
MASS SPECTROMETRY
MHC-II
NEURAL NETWORKS
T CELL EPITOPE
topic ANTIGEN PROCESSING
BINDING PREDICTIONS
ELUTED LIGANDS
MACHINE LEARNING
MASS SPECTROMETRY
MHC-II
NEURAL NETWORKS
T CELL EPITOPE
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv BACKGROUND: Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing. METHODS: We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets. RESULTS: We show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand. CONCLUSIONS: The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands and T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.
Fil: Barra, Carolina M.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Alvarez, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Paul, Sinu. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Buus, Søren. University Of Copenhagen, Faculty Of Health Sciences; Dinamarca
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
description BACKGROUND: Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing. METHODS: We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets. RESULTS: We show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand. CONCLUSIONS: The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands and T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.
publishDate 2018
dc.date.none.fl_str_mv 2018-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/102765
Barra, Carolina M.; Alvarez, Bruno; Paul, Sinu; Sette, Alessandro; Peters, Bjoern; et al.; Footprints of antigen processing boost MHC class II natural ligand predictions; Springer Nature; Genome Medicine; 10; 1; 11-2018
1756-994X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/102765
identifier_str_mv Barra, Carolina M.; Alvarez, Bruno; Paul, Sinu; Sette, Alessandro; Peters, Bjoern; et al.; Footprints of antigen processing boost MHC class II natural ligand predictions; Springer Nature; Genome Medicine; 10; 1; 11-2018
1756-994X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1186/s13073-018-0594-6
info:eu-repo/semantics/altIdentifier/url/https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-018-0594-6
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846083396016537600
score 13.22299

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