Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction

Autores
Barra, Carolina; Ackaert, Chloe; Reynisson, Birkir; Schockaert, Jana; Jessen, Leon Eyrich; Watson, Mark; Jang, Anne; Comtois Marotte, Simon; Goulet, Jean Philippe; Pattijn, Sofie; Paramithiotis, Eustache; Nielsen, Morten
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adverse effects, including allergic reactions and compromised therapeutic efficacy. Production of ADAs is most often associated with activation of CD4 T cell responses resulting from proteolysis of the biotherapeutic and loading of drug-specific peptides into major histocompatibility complex (MHC) class II on professional antigen-presenting cells. Recently, readouts from MHC-associated peptide proteomics (MAPPs) assays have been shown to correlate with the presence of CD4 T cell epitopes. However, the limited sensitivity of MAPPs challenges its use as an immunogenicity biomarker. In this work, MAPPs data was used to construct an artificial neural network (ANN) model for MHC class II antigen presentation. Using Infliximab and Rituximab as showcase stories, the model demonstrated an unprecedented performance for predicting MAPPs and CD4 T cell epitopes in the context of protein-drug immunogenicity, complementing results from MAPPs assays and outperforming conventional prediction models trained on binding affinity data.
Fil: Barra, Carolina. Technical University of Denmark; Dinamarca
Fil: Ackaert, Chloe. No especifíca;
Fil: Reynisson, Birkir. Technical University of Denmark; Dinamarca
Fil: Schockaert, Jana. No especifíca;
Fil: Jessen, Leon Eyrich. Technical University of Denmark; Dinamarca
Fil: Watson, Mark. No especifíca;
Fil: Jang, Anne. No especifíca;
Fil: Comtois Marotte, Simon. No especifíca;
Fil: Goulet, Jean Philippe. No especifíca;
Fil: Pattijn, Sofie. No especifíca;
Fil: Paramithiotis, Eustache. No especifíca;
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
ARTIFICIAL NEURAL-NETWORKS
BIOINFORMATICS
IMMUNOPEPTIDOMICS
MACHINE-LEARNING
MHC-II PREDICTION
PROTEIN-DRUG IMMUNOGENICITY
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:11336/140815

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oai_identifier_str oai:ri.conicet.gov.ar:11336/140815
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity PredictionBarra, CarolinaAckaert, ChloeReynisson, BirkirSchockaert, JanaJessen, Leon EyrichWatson, MarkJang, AnneComtois Marotte, SimonGoulet, Jean PhilippePattijn, SofieParamithiotis, EustacheNielsen, MortenARTIFICIAL NEURAL-NETWORKSBIOINFORMATICSIMMUNOPEPTIDOMICSMACHINE-LEARNINGMHC-II PREDICTIONPROTEIN-DRUG IMMUNOGENICITYhttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adverse effects, including allergic reactions and compromised therapeutic efficacy. Production of ADAs is most often associated with activation of CD4 T cell responses resulting from proteolysis of the biotherapeutic and loading of drug-specific peptides into major histocompatibility complex (MHC) class II on professional antigen-presenting cells. Recently, readouts from MHC-associated peptide proteomics (MAPPs) assays have been shown to correlate with the presence of CD4 T cell epitopes. However, the limited sensitivity of MAPPs challenges its use as an immunogenicity biomarker. In this work, MAPPs data was used to construct an artificial neural network (ANN) model for MHC class II antigen presentation. Using Infliximab and Rituximab as showcase stories, the model demonstrated an unprecedented performance for predicting MAPPs and CD4 T cell epitopes in the context of protein-drug immunogenicity, complementing results from MAPPs assays and outperforming conventional prediction models trained on binding affinity data.Fil: Barra, Carolina. Technical University of Denmark; DinamarcaFil: Ackaert, Chloe. No especifíca;Fil: Reynisson, Birkir. Technical University of Denmark; DinamarcaFil: Schockaert, Jana. No especifíca;Fil: Jessen, Leon Eyrich. Technical University of Denmark; DinamarcaFil: Watson, Mark. No especifíca;Fil: Jang, Anne. No especifíca;Fil: Comtois Marotte, Simon. No especifíca;Fil: Goulet, Jean Philippe. No especifíca;Fil: Pattijn, Sofie. No especifíca;Fil: Paramithiotis, Eustache. No especifíca;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; ArgentinaFrontiers Media S.A.2020-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/140815Barra, Carolina; Ackaert, Chloe; Reynisson, Birkir; Schockaert, Jana; Jessen, Leon Eyrich; et al.; Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction; Frontiers Media S.A.; Frontiers in Immunology; 11; 6-2020; 1-131664-3224CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/article/10.3389/fimmu.2020.01304/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fimmu.2020.01304info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:42:36Zoai:ri.conicet.gov.ar:11336/140815instacron: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:34982025-09-29 09:42:36.987CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction
title Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction
spellingShingle Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction
Barra, Carolina
ARTIFICIAL NEURAL-NETWORKS
BIOINFORMATICS
IMMUNOPEPTIDOMICS
MACHINE-LEARNING
MHC-II PREDICTION
PROTEIN-DRUG IMMUNOGENICITY
title_short Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction
title_full Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction
title_fullStr Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction
title_full_unstemmed Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction
title_sort Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction
dc.creator.none.fl_str_mv Barra, Carolina
Ackaert, Chloe
Reynisson, Birkir
Schockaert, Jana
Jessen, Leon Eyrich
Watson, Mark
Jang, Anne
Comtois Marotte, Simon
Goulet, Jean Philippe
Pattijn, Sofie
Paramithiotis, Eustache
Nielsen, Morten
author Barra, Carolina
author_facet Barra, Carolina
Ackaert, Chloe
Reynisson, Birkir
Schockaert, Jana
Jessen, Leon Eyrich
Watson, Mark
Jang, Anne
Comtois Marotte, Simon
Goulet, Jean Philippe
Pattijn, Sofie
Paramithiotis, Eustache
Nielsen, Morten
author_role author
author2 Ackaert, Chloe
Reynisson, Birkir
Schockaert, Jana
Jessen, Leon Eyrich
Watson, Mark
Jang, Anne
Comtois Marotte, Simon
Goulet, Jean Philippe
Pattijn, Sofie
Paramithiotis, Eustache
Nielsen, Morten
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ARTIFICIAL NEURAL-NETWORKS
BIOINFORMATICS
IMMUNOPEPTIDOMICS
MACHINE-LEARNING
MHC-II PREDICTION
PROTEIN-DRUG IMMUNOGENICITY
topic ARTIFICIAL NEURAL-NETWORKS
BIOINFORMATICS
IMMUNOPEPTIDOMICS
MACHINE-LEARNING
MHC-II PREDICTION
PROTEIN-DRUG IMMUNOGENICITY
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adverse effects, including allergic reactions and compromised therapeutic efficacy. Production of ADAs is most often associated with activation of CD4 T cell responses resulting from proteolysis of the biotherapeutic and loading of drug-specific peptides into major histocompatibility complex (MHC) class II on professional antigen-presenting cells. Recently, readouts from MHC-associated peptide proteomics (MAPPs) assays have been shown to correlate with the presence of CD4 T cell epitopes. However, the limited sensitivity of MAPPs challenges its use as an immunogenicity biomarker. In this work, MAPPs data was used to construct an artificial neural network (ANN) model for MHC class II antigen presentation. Using Infliximab and Rituximab as showcase stories, the model demonstrated an unprecedented performance for predicting MAPPs and CD4 T cell epitopes in the context of protein-drug immunogenicity, complementing results from MAPPs assays and outperforming conventional prediction models trained on binding affinity data.
Fil: Barra, Carolina. Technical University of Denmark; Dinamarca
Fil: Ackaert, Chloe. No especifíca;
Fil: Reynisson, Birkir. Technical University of Denmark; Dinamarca
Fil: Schockaert, Jana. No especifíca;
Fil: Jessen, Leon Eyrich. Technical University of Denmark; Dinamarca
Fil: Watson, Mark. No especifíca;
Fil: Jang, Anne. No especifíca;
Fil: Comtois Marotte, Simon. No especifíca;
Fil: Goulet, Jean Philippe. No especifíca;
Fil: Pattijn, Sofie. No especifíca;
Fil: Paramithiotis, Eustache. No especifíca;
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 Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adverse effects, including allergic reactions and compromised therapeutic efficacy. Production of ADAs is most often associated with activation of CD4 T cell responses resulting from proteolysis of the biotherapeutic and loading of drug-specific peptides into major histocompatibility complex (MHC) class II on professional antigen-presenting cells. Recently, readouts from MHC-associated peptide proteomics (MAPPs) assays have been shown to correlate with the presence of CD4 T cell epitopes. However, the limited sensitivity of MAPPs challenges its use as an immunogenicity biomarker. In this work, MAPPs data was used to construct an artificial neural network (ANN) model for MHC class II antigen presentation. Using Infliximab and Rituximab as showcase stories, the model demonstrated an unprecedented performance for predicting MAPPs and CD4 T cell epitopes in the context of protein-drug immunogenicity, complementing results from MAPPs assays and outperforming conventional prediction models trained on binding affinity data.
publishDate 2020
dc.date.none.fl_str_mv 2020-06
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/11336/140815
Barra, Carolina; Ackaert, Chloe; Reynisson, Birkir; Schockaert, Jana; Jessen, Leon Eyrich; et al.; Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction; Frontiers Media S.A.; Frontiers in Immunology; 11; 6-2020; 1-13
1664-3224
CONICET Digital
CONICET
url http://hdl.handle.net/11336/140815
identifier_str_mv Barra, Carolina; Ackaert, Chloe; Reynisson, Birkir; Schockaert, Jana; Jessen, Leon Eyrich; et al.; Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction; Frontiers Media S.A.; Frontiers in Immunology; 11; 6-2020; 1-13
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.2020.01304/full
info:eu-repo/semantics/altIdentifier/doi/10.3389/fimmu.2020.01304
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 S.A.
publisher.none.fl_str_mv Frontiers Media S.A.
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
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