Gapped sequence alignment using artificial neural networks: Application to the MHC class I system

Motivation: Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8–11 amino acids. On this relative...

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Detalles Bibliográficos
Autores: Andreatta, Massimo, Nielsen, Morten
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/155124
Acceso en línea:http://hdl.handle.net/11336/155124
Access Level:acceso abierto
Palabra clave:SEQUENCE ALIGNMENT
MHC I
INSERTIONS
DELETIONS
PEPTIDE-MHC
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
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oai_identifier_str oai:ri.conicet.gov.ar:11336/155124
network_acronym_str AR
network_name_str Argentina
repository_id_str
spelling Gapped sequence alignment using artificial neural networks: Application to the MHC class I systemAndreatta, MassimoNielsen, MortenSEQUENCE ALIGNMENTMHC IINSERTIONSDELETIONSPEPTIDE-MHChttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Motivation: Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8–11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm. Availability and implementation: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped sequence alignment is publicly available at: http://www.cbs.dtu.dk/ services/NetMHC-4.0.Fil: Andreatta, Massimo. 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; ArgentinaFil: 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; ArgentinaOxford University Press2016-02-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/155124Andreatta, Massimo; Nielsen, Morten; Gapped sequence alignment using artificial neural networks: Application to the MHC class I system; Oxford University Press; Bioinformatics (Oxford, England); 32; 4; 15-2-2016; 511-5171367-48031460-2059CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btv639info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/bioinformatics/article/32/4/511/1744469info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T14:24:45Zoai:ri.conicet.gov.ar:11336/155124instacron: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:34982024-05-08 14:24:46.034CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Gapped sequence alignment using artificial neural networks: Application to the MHC class I system
title Gapped sequence alignment using artificial neural networks: Application to the MHC class I system
spellingShingle Gapped sequence alignment using artificial neural networks: Application to the MHC class I system
Andreatta, Massimo
SEQUENCE ALIGNMENT
MHC I
INSERTIONS
DELETIONS
PEPTIDE-MHC
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
title_short Gapped sequence alignment using artificial neural networks: Application to the MHC class I system
title_full Gapped sequence alignment using artificial neural networks: Application to the MHC class I system
title_fullStr Gapped sequence alignment using artificial neural networks: Application to the MHC class I system
title_full_unstemmed Gapped sequence alignment using artificial neural networks: Application to the MHC class I system
title_sort Gapped sequence alignment using artificial neural networks: Application to the MHC class I system
dc.creator.none.fl_str_mv Andreatta, Massimo
Nielsen, Morten
author Andreatta, Massimo
author_facet Andreatta, Massimo
Nielsen, Morten
author_role author
author2 Nielsen, Morten
author2_role author
dc.subject.none.fl_str_mv SEQUENCE ALIGNMENT
MHC I
INSERTIONS
DELETIONS
PEPTIDE-MHC
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
topic SEQUENCE ALIGNMENT
MHC I
INSERTIONS
DELETIONS
PEPTIDE-MHC
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
description Motivation: Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8–11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm. Availability and implementation: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped sequence alignment is publicly available at: http://www.cbs.dtu.dk/ services/NetMHC-4.0.
publishDate 2016
dc.date.none.fl_str_mv 2016-02-15
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/11336/155124
Andreatta, Massimo; Nielsen, Morten; Gapped sequence alignment using artificial neural networks: Application to the MHC class I system; Oxford University Press; Bioinformatics (Oxford, England); 32; 4; 15-2-2016; 511-517
1367-4803
1460-2059
CONICET Digital
CONICET
url http://hdl.handle.net/11336/155124
identifier_str_mv Andreatta, Massimo; Nielsen, Morten; Gapped sequence alignment using artificial neural networks: Application to the MHC class I system; Oxford University Press; Bioinformatics (Oxford, England); 32; 4; 15-2-2016; 511-517
1367-4803
1460-2059
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btv639
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/bioinformatics/article/32/4/511/1744469
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
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
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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score 15,198674

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