A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:0...

Autores: Pedersen, Lasse Eggers; Rasmussen, Michael; Harndahl, Mikkel; Nielsen, Morten; Buus, Søren; Jungersen, Gregers
Año de publicación: 2016
Idioma: inglés
Tipo de recurso: artículo
Estado: versión publicada
Descripción: Affinity and stability of peptides bound by major histocompatibility complex (MHC) class I molecules are important factors in presentation of peptides to cytotoxic T lymphocytes (CTLs). In silico prediction methods of peptide-MHC binding followed by experimental analysis of peptide-MHC interactions constitute an attractive protocol to select target peptides from the vast pool of viral proteome peptides. We have earlier reported the peptide binding motif of the porcine MHC-I molecules SLA-1*04:01 and SLA-2*04:01, identified by an ELISA affinity-based positional scanning combinatorial peptide library (PSCPL) approach. Here, we report the peptide binding motif of SLA-3*04:01 and combine two prediction methods and analysis of both peptide binding affinity and stability of peptide-MHC complexes to improve rational peptide selection. Using a peptide prediction strategy combining PSCPL binding matrices and in silico prediction algorithms (NetMHCpan), peptide ligands from a repository of 8900 peptides were predicted for binding to SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01 and validated by affinity and stability assays. From the pool of predicted peptides for SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01, a total of 71, 28, and 38 % were binders with affinities below 500 nM, respectively. Comparison of peptide-SLA binding affinity and complex stability showed that peptides of high affinity generally, but not always, produce complexes of high stability. In conclusion, we demonstrate how state-of-the-art prediction and in vitro immunology tools in combination can be used for accurate selection of peptides for MHC class I binding, hence providing an expansion of the field of peptide-MHC analysis also to include pigs as a livestock experimental model.
Fil: Pedersen, Lasse Eggers. Technical University of Denmark; Dinamarca
Fil: Rasmussen, Michael. Universidad de Copenhagen; Dinamarca
Fil: Harndahl, Mikkel. 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. Instituto de Investigaciones Biotecnológicas (subsede Chascomús) | Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas (subsede Chascomús); Argentina
Fil: Buus, Søren. Universidad de Copenhagen; Dinamarca
Fil: Jungersen, Gregers. Technical University of Denmark; Dinamarca
Materia: Affinity
Mhc
Peptide Binding Prediction
Stability
Swine
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:11336/49498

Acceder

id	CONICETDig_44c9b7d511eaf78fe08b73f9ddba99b0
oai_identifier_str	oai:ri.conicet.gov.ar:11336/49498
network_acronym_str	CONICETDig
repository_id_str	3498
network_name_str	CONICET Digital (CONICET)
spelling	A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-304:01Pedersen, Lasse EggersRasmussen, MichaelHarndahl, MikkelNielsen, MortenBuus, SørenJungersen, GregersAffinityMhcPeptide Binding PredictionStabilitySwinehttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Affinity and stability of peptides bound by major histocompatibility complex (MHC) class I molecules are important factors in presentation of peptides to cytotoxic T lymphocytes (CTLs). In silico prediction methods of peptide-MHC binding followed by experimental analysis of peptide-MHC interactions constitute an attractive protocol to select target peptides from the vast pool of viral proteome peptides. We have earlier reported the peptide binding motif of the porcine MHC-I molecules SLA-104:01 and SLA-204:01, identified by an ELISA affinity-based positional scanning combinatorial peptide library (PSCPL) approach. Here, we report the peptide binding motif of SLA-304:01 and combine two prediction methods and analysis of both peptide binding affinity and stability of peptide-MHC complexes to improve rational peptide selection. Using a peptide prediction strategy combining PSCPL binding matrices and in silico prediction algorithms (NetMHCpan), peptide ligands from a repository of 8900 peptides were predicted for binding to SLA-104:01, SLA-204:01, and SLA-304:01 and validated by affinity and stability assays. From the pool of predicted peptides for SLA-104:01, SLA-204:01, and SLA-304:01, a total of 71, 28, and 38 % were binders with affinities below 500 nM, respectively. Comparison of peptide-SLA binding affinity and complex stability showed that peptides of high affinity generally, but not always, produce complexes of high stability. In conclusion, we demonstrate how state-of-the-art prediction and in vitro immunology tools in combination can be used for accurate selection of peptides for MHC class I binding, hence providing an expansion of the field of peptide-MHC analysis also to include pigs as a livestock experimental model.Fil: Pedersen, Lasse Eggers. Technical University of Denmark; DinamarcaFil: Rasmussen, Michael. Universidad de Copenhagen; DinamarcaFil: Harndahl, Mikkel. 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. Instituto de Investigaciones Biotecnológicas (subsede Chascomús) \| Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas (subsede Chascomús); ArgentinaFil: Buus, Søren. Universidad de Copenhagen; DinamarcaFil: Jungersen, Gregers. Technical University of Denmark; DinamarcaSpringer2016-02info: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/11336/49498Pedersen, Lasse Eggers; Rasmussen, Michael; Harndahl, Mikkel; Nielsen, Morten; Buus, Søren; et al.; A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:01; Springer; Immunogenetics; 68; 2; 2-2016; 157-1650093-7711CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s00251-015-0883-9info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs00251-015-0883-9info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:23:08Zoai:ri.conicet.gov.ar:11336/49498instacron: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:34982025-09-10 13:23:09.144CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv	A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:01
title	A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:01
spellingShingle	A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:01 Pedersen, Lasse Eggers Affinity Mhc Peptide Binding Prediction Stability Swine
title_short	A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:01
title_full	A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:01
title_fullStr	A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:01
title_full_unstemmed	A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:01
title_sort	A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:01
dc.creator.none.fl_str_mv	Pedersen, Lasse Eggers Rasmussen, Michael Harndahl, Mikkel Nielsen, Morten Buus, Søren Jungersen, Gregers
author	Pedersen, Lasse Eggers
author_facet	Pedersen, Lasse Eggers Rasmussen, Michael Harndahl, Mikkel Nielsen, Morten Buus, Søren Jungersen, Gregers
author_role	author
author2	Rasmussen, Michael Harndahl, Mikkel Nielsen, Morten Buus, Søren Jungersen, Gregers
author2_role	author author author author author
dc.subject.none.fl_str_mv	Affinity Mhc Peptide Binding Prediction Stability Swine
topic	Affinity Mhc Peptide Binding Prediction Stability Swine
purl_subject.fl_str_mv	https://purl.org/becyt/ford/3.3 https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv	Affinity and stability of peptides bound by major histocompatibility complex (MHC) class I molecules are important factors in presentation of peptides to cytotoxic T lymphocytes (CTLs). In silico prediction methods of peptide-MHC binding followed by experimental analysis of peptide-MHC interactions constitute an attractive protocol to select target peptides from the vast pool of viral proteome peptides. We have earlier reported the peptide binding motif of the porcine MHC-I molecules SLA-104:01 and SLA-204:01, identified by an ELISA affinity-based positional scanning combinatorial peptide library (PSCPL) approach. Here, we report the peptide binding motif of SLA-304:01 and combine two prediction methods and analysis of both peptide binding affinity and stability of peptide-MHC complexes to improve rational peptide selection. Using a peptide prediction strategy combining PSCPL binding matrices and in silico prediction algorithms (NetMHCpan), peptide ligands from a repository of 8900 peptides were predicted for binding to SLA-104:01, SLA-204:01, and SLA-304:01 and validated by affinity and stability assays. From the pool of predicted peptides for SLA-104:01, SLA-204:01, and SLA-3*04:01, a total of 71, 28, and 38 % were binders with affinities below 500 nM, respectively. Comparison of peptide-SLA binding affinity and complex stability showed that peptides of high affinity generally, but not always, produce complexes of high stability. In conclusion, we demonstrate how state-of-the-art prediction and in vitro immunology tools in combination can be used for accurate selection of peptides for MHC class I binding, hence providing an expansion of the field of peptide-MHC analysis also to include pigs as a livestock experimental model. Fil: Pedersen, Lasse Eggers. Technical University of Denmark; Dinamarca Fil: Rasmussen, Michael. Universidad de Copenhagen; Dinamarca Fil: Harndahl, Mikkel. 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. Instituto de Investigaciones Biotecnológicas (subsede Chascomús) \| Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas (subsede Chascomús); Argentina Fil: Buus, Søren. Universidad de Copenhagen; Dinamarca Fil: Jungersen, Gregers. Technical University of Denmark; Dinamarca
description	Affinity and stability of peptides bound by major histocompatibility complex (MHC) class I molecules are important factors in presentation of peptides to cytotoxic T lymphocytes (CTLs). In silico prediction methods of peptide-MHC binding followed by experimental analysis of peptide-MHC interactions constitute an attractive protocol to select target peptides from the vast pool of viral proteome peptides. We have earlier reported the peptide binding motif of the porcine MHC-I molecules SLA-104:01 and SLA-204:01, identified by an ELISA affinity-based positional scanning combinatorial peptide library (PSCPL) approach. Here, we report the peptide binding motif of SLA-304:01 and combine two prediction methods and analysis of both peptide binding affinity and stability of peptide-MHC complexes to improve rational peptide selection. Using a peptide prediction strategy combining PSCPL binding matrices and in silico prediction algorithms (NetMHCpan), peptide ligands from a repository of 8900 peptides were predicted for binding to SLA-104:01, SLA-204:01, and SLA-304:01 and validated by affinity and stability assays. From the pool of predicted peptides for SLA-104:01, SLA-204:01, and SLA-3*04:01, a total of 71, 28, and 38 % were binders with affinities below 500 nM, respectively. Comparison of peptide-SLA binding affinity and complex stability showed that peptides of high affinity generally, but not always, produce complexes of high stability. In conclusion, we demonstrate how state-of-the-art prediction and in vitro immunology tools in combination can be used for accurate selection of peptides for MHC class I binding, hence providing an expansion of the field of peptide-MHC analysis also to include pigs as a livestock experimental model.
publishDate	2016
dc.date.none.fl_str_mv	2016-02
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/11336/49498 Pedersen, Lasse Eggers; Rasmussen, Michael; Harndahl, Mikkel; Nielsen, Morten; Buus, Søren; et al.; A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:01; Springer; Immunogenetics; 68; 2; 2-2016; 157-165 0093-7711 CONICET Digital CONICET
url	http://hdl.handle.net/11336/49498
identifier_str_mv	Pedersen, Lasse Eggers; Rasmussen, Michael; Harndahl, Mikkel; Nielsen, Morten; Buus, Søren; et al.; A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:01; Springer; Immunogenetics; 68; 2; 2-2016; 157-165 0093-7711 CONICET Digital CONICET
dc.language.none.fl_str_mv	eng
language	eng
dc.relation.none.fl_str_mv	info:eu-repo/semantics/altIdentifier/doi/10.1007/s00251-015-0883-9 info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs00251-015-0883-9
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	Springer
publisher.none.fl_str_mv	Springer
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_	1842981276369289216
score	12.48226

A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-1*04:01, SLA-2*04:01, and SLA-3*04:0...

Publicaciones similares

A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-104:01, SLA-204:01, and SLA-3*04:0...