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dc.contributor.author
Nielsen, Morten
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dc.contributor.author
Connelley, Tim
dc.contributor.author
Ternette, Nicola
dc.date.available
2020-02-07T19:00:47Z
dc.date.issued
2018-01
dc.identifier.citation
Nielsen, Morten; Connelley, Tim; Ternette, Nicola; Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding Data; American Chemical Society; Journal of Proteome Research; 17; 1; 1-2018; 559-567
dc.identifier.issn
1535-3893
dc.identifier.uri
http://hdl.handle.net/11336/96914
dc.description.abstract
Peptide binding to MHC class I molecules is the single most selective step in antigen presentation and the strongest single correlate to peptide cellular immunogenicity. The cost of experimentally characterizing the rules of peptide presentation for a given MHC-I molecule is extensive, and predictors of peptide-MHC interactions constitute an attractive alternative. Recently, an increasing amount of MHC presented peptides identified by mass spectrometry (MS ligands) has been published. Handling and interpretation of MS ligand data is, in general, challenging due to the polyspecificity nature of the data. We here outline a general pipeline for dealing with this challenge and accurately annotate ligands to the relevant MHC-I molecule they were eluted from by use of GibbsClustering and binding motif information inferred from in silico models. We illustrate the approach here in the context of MHC-I molecules (BoLA) of cattle. Next, we demonstrate how such annotated BoLA MS ligand data can readily be integrated with in vitro binding affinity data in a prediction model with very high and unprecedented performance for identification of BoLA-I restricted T-cell epitopes. The prediction model is freely available at http://www.cbs.dtu.dk/services/NetMHCpan/NetBoLApan. The approach has here been applied to the BoLA-I system, but the pipeline is readily applicable to MHC systems in other species.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
American Chemical Society
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ANTIGEN PRESENTATION
dc.subject
BIOINFORMATICS
dc.subject
BOLA
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GIBBSCLUSTERING
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MASS SPECTROMETRY
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MHC
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NETMHCPAN
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PREDICTION
dc.subject
T-CELL EPITOPES
dc.subject.classification
Otras Ciencias de la Salud
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dc.subject.classification
Ciencias de la Salud
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CIENCIAS MÉDICAS Y DE LA SALUD
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dc.title
Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding 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
2019-11-25T17:45:28Z
dc.journal.volume
17
dc.journal.number
1
dc.journal.pagination
559-567
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentina
dc.description.fil
Fil: Connelley, Tim. The Roslin Institute; Reino Unido
dc.description.fil
Fil: Ternette, Nicola. The Jenner Institute; Reino Unido
dc.journal.title
Journal of Proteome Research
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1021/acs.jproteome.7b00675
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acs.jproteome.7b00675
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