Improved methods for predicting peptide binding affinity to MHC class II molecules

Autores
Jensen, Kamilla Kjærgaard; Andreatta, Massimo; Marcatili, Paolo; Buus, Søren; Greenbaum, Jason A.; Yan, Zhen; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC–II–peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC–peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.
Fil: Jensen, Kamilla Kjærgaard. Technical University of Denmark; Dinamarca
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: Marcatili, Paolo. Technical University of Denmark; Dinamarca
Fil: Buus, Søren. University of Copenhagen; Dinamarca
Fil: Greenbaum, Jason A.. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Yan, Zhen. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Sette, Alessandro. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Peters, Bjoern. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Nielsen, Morten. 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. Technical University of Denmark; Dinamarca
Materia
AFFINITY PREDICTIONS
IMMUNOGENIC PEPTIDES
MHC BINDING SPECIFICITY
PEPTIDE-MHC BINDING
T-CELL EPITOPE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/96635
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/96635
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Improved methods for predicting peptide binding affinity to MHC class II moleculesJensen, Kamilla KjærgaardAndreatta, MassimoMarcatili, PaoloBuus, SørenGreenbaum, Jason A.Yan, ZhenSette, AlessandroPeters, BjoernNielsen, MortenAFFINITY PREDICTIONSIMMUNOGENIC PEPTIDESMHC BINDING SPECIFICITYPEPTIDE-MHC BINDINGT-CELL EPITOPEhttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC–II–peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC–peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.Fil: Jensen, Kamilla Kjærgaard. Technical University of Denmark; DinamarcaFil: 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: Marcatili, Paolo. Technical University of Denmark; DinamarcaFil: Buus, Søren. University of Copenhagen; DinamarcaFil: Greenbaum, Jason A.. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Yan, Zhen. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Sette, Alessandro. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Peters, Bjoern. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Nielsen, Morten. 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. Technical University of Denmark; DinamarcaWiley Blackwell Publishing, Inc2018-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/96635Jensen, Kamilla Kjærgaard; Andreatta, Massimo; Marcatili, Paolo; Buus, Søren; Greenbaum, Jason A.; et al.; Improved methods for predicting peptide binding affinity to MHC class II molecules; Wiley Blackwell Publishing, Inc; Immunology; 154; 3; 7-2018; 394-4060019-2805CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://doi.wiley.com/10.1111/imm.12889info:eu-repo/semantics/altIdentifier/doi/10.1111/imm.12889info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:44:54Zoai:ri.conicet.gov.ar:11336/96635instacron: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:34982025-09-03 09:44:54.289CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Improved methods for predicting peptide binding affinity to MHC class II molecules
title Improved methods for predicting peptide binding affinity to MHC class II molecules
spellingShingle Improved methods for predicting peptide binding affinity to MHC class II molecules
Jensen, Kamilla Kjærgaard
AFFINITY PREDICTIONS
IMMUNOGENIC PEPTIDES
MHC BINDING SPECIFICITY
PEPTIDE-MHC BINDING
T-CELL EPITOPE
title_short Improved methods for predicting peptide binding affinity to MHC class II molecules
title_full Improved methods for predicting peptide binding affinity to MHC class II molecules
title_fullStr Improved methods for predicting peptide binding affinity to MHC class II molecules
title_full_unstemmed Improved methods for predicting peptide binding affinity to MHC class II molecules
title_sort Improved methods for predicting peptide binding affinity to MHC class II molecules
dc.creator.none.fl_str_mv Jensen, Kamilla Kjærgaard
Andreatta, Massimo
Marcatili, Paolo
Buus, Søren
Greenbaum, Jason A.
Yan, Zhen
Sette, Alessandro
Peters, Bjoern
Nielsen, Morten
author Jensen, Kamilla Kjærgaard
author_facet Jensen, Kamilla Kjærgaard
Andreatta, Massimo
Marcatili, Paolo
Buus, Søren
Greenbaum, Jason A.
Yan, Zhen
Sette, Alessandro
Peters, Bjoern
Nielsen, Morten
author_role author
author2 Andreatta, Massimo
Marcatili, Paolo
Buus, Søren
Greenbaum, Jason A.
Yan, Zhen
Sette, Alessandro
Peters, Bjoern
Nielsen, Morten
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv AFFINITY PREDICTIONS
IMMUNOGENIC PEPTIDES
MHC BINDING SPECIFICITY
PEPTIDE-MHC BINDING
T-CELL EPITOPE
topic AFFINITY PREDICTIONS
IMMUNOGENIC PEPTIDES
MHC BINDING SPECIFICITY
PEPTIDE-MHC BINDING
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 Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC–II–peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC–peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.
Fil: Jensen, Kamilla Kjærgaard. Technical University of Denmark; Dinamarca
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: Marcatili, Paolo. Technical University of Denmark; Dinamarca
Fil: Buus, Søren. University of Copenhagen; Dinamarca
Fil: Greenbaum, Jason A.. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Yan, Zhen. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Sette, Alessandro. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Peters, Bjoern. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Nielsen, Morten. 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. Technical University of Denmark; Dinamarca
description Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC–II–peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC–peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.
publishDate 2018
dc.date.none.fl_str_mv 2018-07
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/11336/96635
Jensen, Kamilla Kjærgaard; Andreatta, Massimo; Marcatili, Paolo; Buus, Søren; Greenbaum, Jason A.; et al.; Improved methods for predicting peptide binding affinity to MHC class II molecules; Wiley Blackwell Publishing, Inc; Immunology; 154; 3; 7-2018; 394-406
0019-2805
CONICET Digital
CONICET
url http://hdl.handle.net/11336/96635
identifier_str_mv Jensen, Kamilla Kjærgaard; Andreatta, Massimo; Marcatili, Paolo; Buus, Søren; Greenbaum, Jason A.; et al.; Improved methods for predicting peptide binding affinity to MHC class II molecules; Wiley Blackwell Publishing, Inc; Immunology; 154; 3; 7-2018; 394-406
0019-2805
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://doi.wiley.com/10.1111/imm.12889
info:eu-repo/semantics/altIdentifier/doi/10.1111/imm.12889
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
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_ 1842268696325652480
score 13.13397

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