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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/140815
Ver los metadatos del registro completo
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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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1844613342047502336 |
score |
13.070432 |