Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy
- Autores
- Mattsson, Andreas Holm; Kringelum, J.V.; Garde, C.; Nielsen, Morten
- Año de publicación
- 2016
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Pan-specific prediction of receptor–ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing with overfitting and data redundancy is critical. Most often so-called ligand clustering methods have been used to deal with these issues in the context of pan-specific receptor–ligand predictions, and the MHC system the approach has proven highly effective for extrapolating information from a limited set of receptors with well characterized binding motifs, to others with no or very limited experimental characterization. The success of this approach has however proven to depend strongly on the similarity of the query molecule to the molecules with characterized specificity using in the machine-learning process. Here, we outline an alternative strategy with the aim of altering this and construct data sets optimal for training of pan-specific receptor–ligand predictions focusing on receptor similarity rather than ligand similarity. We show that this receptor clustering method consistently in benchmarks covering affinity predictions, MHC ligand and MHC epitope identification perform better than the conventional ligand clustering method on the alleles with remote similarity to the training set.
Fil: Mattsson, Andreas Holm. Technical University of Denmark; Dinamarca. Evaxion Biotech; Dinamarca
Fil: Kringelum, J.V.. Evaxion Biotech; Dinamarca
Fil: Garde, C.. Universidad de Copenhagen; Dinamarca
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. 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 - Materia
-
Artificial Neural Networks
Clustering
Mhc Binding Specificity
Mhc Class I
Peptide–Mhc Binding
T-Cell Epitope - 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:11336/48877
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Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategyMattsson, Andreas HolmKringelum, J.V.Garde, C.Nielsen, MortenArtificial Neural NetworksClusteringMhc Binding SpecificityMhc Class IPeptide–Mhc BindingT-Cell Epitopehttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Pan-specific prediction of receptor–ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing with overfitting and data redundancy is critical. Most often so-called ligand clustering methods have been used to deal with these issues in the context of pan-specific receptor–ligand predictions, and the MHC system the approach has proven highly effective for extrapolating information from a limited set of receptors with well characterized binding motifs, to others with no or very limited experimental characterization. The success of this approach has however proven to depend strongly on the similarity of the query molecule to the molecules with characterized specificity using in the machine-learning process. Here, we outline an alternative strategy with the aim of altering this and construct data sets optimal for training of pan-specific receptor–ligand predictions focusing on receptor similarity rather than ligand similarity. We show that this receptor clustering method consistently in benchmarks covering affinity predictions, MHC ligand and MHC epitope identification perform better than the conventional ligand clustering method on the alleles with remote similarity to the training set.Fil: Mattsson, Andreas Holm. Technical University of Denmark; Dinamarca. Evaxion Biotech; DinamarcaFil: Kringelum, J.V.. Evaxion Biotech; DinamarcaFil: Garde, C.. Universidad de Copenhagen; DinamarcaFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. 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; ArgentinaWiley Blackwell Publishing, Inc2016-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/48877Mattsson, Andreas Holm; Kringelum, J.V.; Garde, C.; Nielsen, Morten; Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy; Wiley Blackwell Publishing, Inc; HLA; 88; 6; 12-2016; 287-2922059-2310CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1111/tan.12911info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1111/tan.12911info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:56:21Zoai:ri.conicet.gov.ar:11336/48877instacron: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 09:56:22.156CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy |
title |
Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy |
spellingShingle |
Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy Mattsson, Andreas Holm Artificial Neural Networks Clustering Mhc Binding Specificity Mhc Class I Peptide–Mhc Binding T-Cell Epitope |
title_short |
Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy |
title_full |
Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy |
title_fullStr |
Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy |
title_full_unstemmed |
Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy |
title_sort |
Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy |
dc.creator.none.fl_str_mv |
Mattsson, Andreas Holm Kringelum, J.V. Garde, C. Nielsen, Morten |
author |
Mattsson, Andreas Holm |
author_facet |
Mattsson, Andreas Holm Kringelum, J.V. Garde, C. Nielsen, Morten |
author_role |
author |
author2 |
Kringelum, J.V. Garde, C. Nielsen, Morten |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
Artificial Neural Networks Clustering Mhc Binding Specificity Mhc Class I Peptide–Mhc Binding T-Cell Epitope |
topic |
Artificial Neural Networks Clustering Mhc Binding Specificity Mhc Class I Peptide–Mhc Binding T-Cell Epitope |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/3.3 https://purl.org/becyt/ford/3 |
dc.description.none.fl_txt_mv |
Pan-specific prediction of receptor–ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing with overfitting and data redundancy is critical. Most often so-called ligand clustering methods have been used to deal with these issues in the context of pan-specific receptor–ligand predictions, and the MHC system the approach has proven highly effective for extrapolating information from a limited set of receptors with well characterized binding motifs, to others with no or very limited experimental characterization. The success of this approach has however proven to depend strongly on the similarity of the query molecule to the molecules with characterized specificity using in the machine-learning process. Here, we outline an alternative strategy with the aim of altering this and construct data sets optimal for training of pan-specific receptor–ligand predictions focusing on receptor similarity rather than ligand similarity. We show that this receptor clustering method consistently in benchmarks covering affinity predictions, MHC ligand and MHC epitope identification perform better than the conventional ligand clustering method on the alleles with remote similarity to the training set. Fil: Mattsson, Andreas Holm. Technical University of Denmark; Dinamarca. Evaxion Biotech; Dinamarca Fil: Kringelum, J.V.. Evaxion Biotech; Dinamarca Fil: Garde, C.. Universidad de Copenhagen; Dinamarca Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. 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 |
description |
Pan-specific prediction of receptor–ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing with overfitting and data redundancy is critical. Most often so-called ligand clustering methods have been used to deal with these issues in the context of pan-specific receptor–ligand predictions, and the MHC system the approach has proven highly effective for extrapolating information from a limited set of receptors with well characterized binding motifs, to others with no or very limited experimental characterization. The success of this approach has however proven to depend strongly on the similarity of the query molecule to the molecules with characterized specificity using in the machine-learning process. Here, we outline an alternative strategy with the aim of altering this and construct data sets optimal for training of pan-specific receptor–ligand predictions focusing on receptor similarity rather than ligand similarity. We show that this receptor clustering method consistently in benchmarks covering affinity predictions, MHC ligand and MHC epitope identification perform better than the conventional ligand clustering method on the alleles with remote similarity to the training set. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-12 |
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/11336/48877 Mattsson, Andreas Holm; Kringelum, J.V.; Garde, C.; Nielsen, Morten; Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy; Wiley Blackwell Publishing, Inc; HLA; 88; 6; 12-2016; 287-292 2059-2310 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/48877 |
identifier_str_mv |
Mattsson, Andreas Holm; Kringelum, J.V.; Garde, C.; Nielsen, Morten; Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy; Wiley Blackwell Publishing, Inc; HLA; 88; 6; 12-2016; 287-292 2059-2310 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1111/tan.12911 info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1111/tan.12911 |
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 |
Wiley Blackwell Publishing, Inc |
publisher.none.fl_str_mv |
Wiley Blackwell Publishing, Inc |
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_ |
1842269400637374464 |
score |
13.13397 |