Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction

Autores
Wendorff, Mareike; García Álvarez, Heli Magalí; Østerbye, Thomas; ElAbd, Hesham; Rosati, Elisa; Degenhardt, Frauke; Buus, Søren; Franke, Andre; Nielsen, Morten
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unprecedented level of high-throughput. Microarrays with over 200,000 defined peptides were assayed with four exemplary HLA-II molecules. Machine learning was applied to mine the signals. The comparison of identified binding motifs, and power for predicting eluted ligands and CD4+ epitope datasets to that obtained using NetMHCIIpan-3.2, confirmed a high quality of the chip readout. These results suggest that the proposed microarray technology offers a novel and unique platform for large-scale unbiased interrogation of peptide binding preferences of HLA-II molecules.
Fil: Wendorff, Mareike. Christian Albrechts Universitat Zu Kiel.; Alemania
Fil: García Álvarez, Heli Magalí. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Østerbye, Thomas. Universidad de Copenhagen; Dinamarca
Fil: ElAbd, Hesham. Christian Albrechts Universitat Zu Kiel.; Alemania
Fil: Rosati, Elisa. Christian Albrechts Universitat Zu Kiel.; Alemania
Fil: Degenhardt, Frauke. Christian Albrechts Universitat Zu Kiel.; Alemania
Fil: Buus, Søren. Universidad de Copenhagen; Dinamarca
Fil: Franke, Andre. Christian Albrechts Universitat Zu Kiel.; Alemania
Fil: Nielsen, Morten. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Materia
Ultra-high density peptide microarray
MHC class II
Antigen presentation
HLA
Machine learning
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:11336/267498
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/267498
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope PredictionWendorff, MareikeGarcía Álvarez, Heli MagalíØsterbye, ThomasElAbd, HeshamRosati, ElisaDegenhardt, FraukeBuus, SørenFranke, AndreNielsen, MortenUltra-high density peptide microarrayMHC class IIAntigen presentationHLAMachine learninghttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unprecedented level of high-throughput. Microarrays with over 200,000 defined peptides were assayed with four exemplary HLA-II molecules. Machine learning was applied to mine the signals. The comparison of identified binding motifs, and power for predicting eluted ligands and CD4+ epitope datasets to that obtained using NetMHCIIpan-3.2, confirmed a high quality of the chip readout. These results suggest that the proposed microarray technology offers a novel and unique platform for large-scale unbiased interrogation of peptide binding preferences of HLA-II molecules.Fil: Wendorff, Mareike. Christian Albrechts Universitat Zu Kiel.; AlemaniaFil: García Álvarez, Heli Magalí. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Østerbye, Thomas. Universidad de Copenhagen; DinamarcaFil: ElAbd, Hesham. Christian Albrechts Universitat Zu Kiel.; AlemaniaFil: Rosati, Elisa. Christian Albrechts Universitat Zu Kiel.; AlemaniaFil: Degenhardt, Frauke. Christian Albrechts Universitat Zu Kiel.; AlemaniaFil: Buus, Søren. Universidad de Copenhagen; DinamarcaFil: Franke, Andre. Christian Albrechts Universitat Zu Kiel.; AlemaniaFil: Nielsen, Morten. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; ArgentinaFrontiers Media2020-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/267498Wendorff, Mareike; García Álvarez, Heli Magalí; Østerbye, Thomas; ElAbd, Hesham; Rosati, Elisa; et al.; Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction; Frontiers Media; Frontiers in Immunology; 11; 8-2020; 1-81664-3224CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/article/10.3389/fimmu.2020.01705/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fimmu.2020.01705info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:44:19Zoai:ri.conicet.gov.ar:11336/267498instacron: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 09:44:20.249CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction
title Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction
spellingShingle Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction
Wendorff, Mareike
Ultra-high density peptide microarray
MHC class II
Antigen presentation
HLA
Machine learning
title_short Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction
title_full Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction
title_fullStr Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction
title_full_unstemmed Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction
title_sort Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction
dc.creator.none.fl_str_mv Wendorff, Mareike
García Álvarez, Heli Magalí
Østerbye, Thomas
ElAbd, Hesham
Rosati, Elisa
Degenhardt, Frauke
Buus, Søren
Franke, Andre
Nielsen, Morten
author Wendorff, Mareike
author_facet Wendorff, Mareike
García Álvarez, Heli Magalí
Østerbye, Thomas
ElAbd, Hesham
Rosati, Elisa
Degenhardt, Frauke
Buus, Søren
Franke, Andre
Nielsen, Morten
author_role author
author2 García Álvarez, Heli Magalí
Østerbye, Thomas
ElAbd, Hesham
Rosati, Elisa
Degenhardt, Frauke
Buus, Søren
Franke, Andre
Nielsen, Morten
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Ultra-high density peptide microarray
MHC class II
Antigen presentation
HLA
Machine learning
topic Ultra-high density peptide microarray
MHC class II
Antigen presentation
HLA
Machine learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unprecedented level of high-throughput. Microarrays with over 200,000 defined peptides were assayed with four exemplary HLA-II molecules. Machine learning was applied to mine the signals. The comparison of identified binding motifs, and power for predicting eluted ligands and CD4+ epitope datasets to that obtained using NetMHCIIpan-3.2, confirmed a high quality of the chip readout. These results suggest that the proposed microarray technology offers a novel and unique platform for large-scale unbiased interrogation of peptide binding preferences of HLA-II molecules.
Fil: Wendorff, Mareike. Christian Albrechts Universitat Zu Kiel.; Alemania
Fil: García Álvarez, Heli Magalí. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Østerbye, Thomas. Universidad de Copenhagen; Dinamarca
Fil: ElAbd, Hesham. Christian Albrechts Universitat Zu Kiel.; Alemania
Fil: Rosati, Elisa. Christian Albrechts Universitat Zu Kiel.; Alemania
Fil: Degenhardt, Frauke. Christian Albrechts Universitat Zu Kiel.; Alemania
Fil: Buus, Søren. Universidad de Copenhagen; Dinamarca
Fil: Franke, Andre. Christian Albrechts Universitat Zu Kiel.; Alemania
Fil: Nielsen, Morten. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
description Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unprecedented level of high-throughput. Microarrays with over 200,000 defined peptides were assayed with four exemplary HLA-II molecules. Machine learning was applied to mine the signals. The comparison of identified binding motifs, and power for predicting eluted ligands and CD4+ epitope datasets to that obtained using NetMHCIIpan-3.2, confirmed a high quality of the chip readout. These results suggest that the proposed microarray technology offers a novel and unique platform for large-scale unbiased interrogation of peptide binding preferences of HLA-II molecules.
publishDate 2020
dc.date.none.fl_str_mv 2020-08
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/11336/267498
Wendorff, Mareike; García Álvarez, Heli Magalí; Østerbye, Thomas; ElAbd, Hesham; Rosati, Elisa; et al.; Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction; Frontiers Media; Frontiers in Immunology; 11; 8-2020; 1-8
1664-3224
CONICET Digital
CONICET
url http://hdl.handle.net/11336/267498
identifier_str_mv Wendorff, Mareike; García Álvarez, Heli Magalí; Østerbye, Thomas; ElAbd, Hesham; Rosati, Elisa; et al.; Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction; Frontiers Media; Frontiers in Immunology; 11; 8-2020; 1-8
1664-3224
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/article/10.3389/fimmu.2020.01705/full
info:eu-repo/semantics/altIdentifier/doi/10.3389/fimmu.2020.01705
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
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
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_ 1842268659626541056
score 13.13397

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