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dc.contributor.author
Reynisson, Birkir
dc.contributor.author
Barra, Carolina
dc.contributor.author
Kaabinejadian, Saghar
dc.contributor.author
Hildebrand, William H.
dc.contributor.author
Peters, Bjoern
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dc.contributor.author
Nielsen, Morten
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dc.date.available
2020-08-28T18:02:50Z
dc.date.issued
2020-06
dc.identifier.citation
Reynisson, Birkir; Barra, Carolina; Kaabinejadian, Saghar; Hildebrand, William H.; Peters, Bjoern; et al.; Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data; American Chemical Society; Journal of Proteome Research; 19; 6; 6-2020; 2304-2315
dc.identifier.issn
1535-3893
dc.identifier.uri
http://hdl.handle.net/11336/112657
dc.description.abstract
Major histocompatibility complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyze and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Furthermore, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0 demonstrated improved performance beyond current state-of-the-art predictors for ligand and CD4+ T cell epitope prediction. These results suggest that NNAlign_MA and NetMHCIIpan-4.0 are powerful tools for analysis of immunopeptidome MS data, prediction of T cell epitopes, and development of personalized immunotherapies.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
American Chemical Society
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ANTIGEN PRESENTATION
dc.subject
BIOINFORMATICS
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IMMUNOINFORMATICS
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IMMUNOLOGY
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IMMUNOPEPTIDOMICS
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MACHINE LEARNING
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MASS SPECTROMETRY
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MHC II
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NEOEPITOPES
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Otras Ciencias de la Salud
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dc.subject.classification
Ciencias de la Salud
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dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
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dc.title
Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-07-01T17:02:23Z
dc.journal.volume
19
dc.journal.number
6
dc.journal.pagination
2304-2315
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Reynisson, Birkir. Technical University of Denmark; Dinamarca
dc.description.fil
Fil: Barra, Carolina. Technical University of Denmark; Dinamarca
dc.description.fil
Fil: Kaabinejadian, Saghar. Pure Mhc Llc; Estados Unidos
dc.description.fil
Fil: Hildebrand, William H.. University Of Oklahoma. Health Sciences Center; Estados Unidos
dc.description.fil
Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos. University of California; Estados Unidos
dc.description.fil
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
dc.journal.title
Journal of Proteome Research
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acs.jproteome.9b00874
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1021/acs.jproteome.9b00874
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