NetTepi: an integrated method for the prediction of T-cell epitopes
- Autores
- Trolle, Thomas; Nielsen, Morten
- Año de publicación
- 2014
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Multiple factors determine the ability of a peptide to elicit a cytotoxic T cell lymphocyte response. Binding to a major histocompatibility complex class I (MHC-I) molecule is one of the most essential factors, as no peptide can become a T cell epitope unless presented on the cell surface in complex with an MHC-I molecule. As such, peptide-MHC (pMHC) binding affinity predictors are currently the premier methods for T cell epitope prediction, and these prediction methods have been shown to have high predictive performances in multiple studies. However, not all MHC-I binders are T cell epitopes, and multiple studies have investigated what additional factors are important for determining the immunogenicity of a peptide. A recent study suggested that pMHC stability plays an important role in determining if a peptide can become a T cell epitope. Likewise, a T cell propensity model has been proposed for identifying MHC binding peptides with amino acid compositions favoring T cell receptor interactions. In this study, we investigate if improved accuracy for T cell epitope discovery can be achieved by integrating predictions for pMHC binding affinity, pMHC stability, and T cell propensity. We show that a weighted sum approach allows pMHC stability and T cell propensity predictions to enrich pMHC binding affinity predictions. The integrated model leads to a consistent and significant increase in predictive performance and we demonstrate how this can be utilized to decrease the experimental workload of epitope screens. The final method, NetTepi, is publically available at www.cbs.dtu.dk/services/NetTepi
Fil: Trolle, Thomas. Technical University Of Denmark; Dinamarca
Fil: Nielsen, Morten. Universidad Nacional de San Martín; Argentina. 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
-
T Cell Epitope
Peptide Immunogenicity
Mhc Binding Specificity
Peptide-Mhc Binding Stability
Cytotoxic T Lymphocyte
Mhc Class I - 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:11336/17886
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NetTepi: an integrated method for the prediction of T-cell epitopesTrolle, ThomasNielsen, MortenT Cell EpitopePeptide ImmunogenicityMhc Binding SpecificityPeptide-Mhc Binding StabilityCytotoxic T LymphocyteMhc Class Ihttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Multiple factors determine the ability of a peptide to elicit a cytotoxic T cell lymphocyte response. Binding to a major histocompatibility complex class I (MHC-I) molecule is one of the most essential factors, as no peptide can become a T cell epitope unless presented on the cell surface in complex with an MHC-I molecule. As such, peptide-MHC (pMHC) binding affinity predictors are currently the premier methods for T cell epitope prediction, and these prediction methods have been shown to have high predictive performances in multiple studies. However, not all MHC-I binders are T cell epitopes, and multiple studies have investigated what additional factors are important for determining the immunogenicity of a peptide. A recent study suggested that pMHC stability plays an important role in determining if a peptide can become a T cell epitope. Likewise, a T cell propensity model has been proposed for identifying MHC binding peptides with amino acid compositions favoring T cell receptor interactions. In this study, we investigate if improved accuracy for T cell epitope discovery can be achieved by integrating predictions for pMHC binding affinity, pMHC stability, and T cell propensity. We show that a weighted sum approach allows pMHC stability and T cell propensity predictions to enrich pMHC binding affinity predictions. The integrated model leads to a consistent and significant increase in predictive performance and we demonstrate how this can be utilized to decrease the experimental workload of epitope screens. The final method, NetTepi, is publically available at www.cbs.dtu.dk/services/NetTepiFil: Trolle, Thomas. Technical University Of Denmark; DinamarcaFil: Nielsen, Morten. Universidad Nacional de San Martín; Argentina. 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; ArgentinaSpringer Verlag Berlín2014-08info: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/11336/17886Trolle, Thomas; Nielsen, Morten; NetTepi: an integrated method for the prediction of T-cell epitopes; Springer Verlag Berlín; Immunogenetics; 66; 7; 8-2014; 449-4560093-77111432-1211enginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs00251-014-0779-0info:eu-repo/semantics/altIdentifier/doi/10.1007/s00251-014-0779-0info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:55:22Zoai:ri.conicet.gov.ar:11336/17886instacron: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 09:55:22.66CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
NetTepi: an integrated method for the prediction of T-cell epitopes |
title |
NetTepi: an integrated method for the prediction of T-cell epitopes |
spellingShingle |
NetTepi: an integrated method for the prediction of T-cell epitopes Trolle, Thomas T Cell Epitope Peptide Immunogenicity Mhc Binding Specificity Peptide-Mhc Binding Stability Cytotoxic T Lymphocyte Mhc Class I |
title_short |
NetTepi: an integrated method for the prediction of T-cell epitopes |
title_full |
NetTepi: an integrated method for the prediction of T-cell epitopes |
title_fullStr |
NetTepi: an integrated method for the prediction of T-cell epitopes |
title_full_unstemmed |
NetTepi: an integrated method for the prediction of T-cell epitopes |
title_sort |
NetTepi: an integrated method for the prediction of T-cell epitopes |
dc.creator.none.fl_str_mv |
Trolle, Thomas Nielsen, Morten |
author |
Trolle, Thomas |
author_facet |
Trolle, Thomas Nielsen, Morten |
author_role |
author |
author2 |
Nielsen, Morten |
author2_role |
author |
dc.subject.none.fl_str_mv |
T Cell Epitope Peptide Immunogenicity Mhc Binding Specificity Peptide-Mhc Binding Stability Cytotoxic T Lymphocyte Mhc Class I |
topic |
T Cell Epitope Peptide Immunogenicity Mhc Binding Specificity Peptide-Mhc Binding Stability Cytotoxic T Lymphocyte Mhc Class I |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/3.3 https://purl.org/becyt/ford/3 |
dc.description.none.fl_txt_mv |
Multiple factors determine the ability of a peptide to elicit a cytotoxic T cell lymphocyte response. Binding to a major histocompatibility complex class I (MHC-I) molecule is one of the most essential factors, as no peptide can become a T cell epitope unless presented on the cell surface in complex with an MHC-I molecule. As such, peptide-MHC (pMHC) binding affinity predictors are currently the premier methods for T cell epitope prediction, and these prediction methods have been shown to have high predictive performances in multiple studies. However, not all MHC-I binders are T cell epitopes, and multiple studies have investigated what additional factors are important for determining the immunogenicity of a peptide. A recent study suggested that pMHC stability plays an important role in determining if a peptide can become a T cell epitope. Likewise, a T cell propensity model has been proposed for identifying MHC binding peptides with amino acid compositions favoring T cell receptor interactions. In this study, we investigate if improved accuracy for T cell epitope discovery can be achieved by integrating predictions for pMHC binding affinity, pMHC stability, and T cell propensity. We show that a weighted sum approach allows pMHC stability and T cell propensity predictions to enrich pMHC binding affinity predictions. The integrated model leads to a consistent and significant increase in predictive performance and we demonstrate how this can be utilized to decrease the experimental workload of epitope screens. The final method, NetTepi, is publically available at www.cbs.dtu.dk/services/NetTepi Fil: Trolle, Thomas. Technical University Of Denmark; Dinamarca Fil: Nielsen, Morten. Universidad Nacional de San Martín; Argentina. 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 |
Multiple factors determine the ability of a peptide to elicit a cytotoxic T cell lymphocyte response. Binding to a major histocompatibility complex class I (MHC-I) molecule is one of the most essential factors, as no peptide can become a T cell epitope unless presented on the cell surface in complex with an MHC-I molecule. As such, peptide-MHC (pMHC) binding affinity predictors are currently the premier methods for T cell epitope prediction, and these prediction methods have been shown to have high predictive performances in multiple studies. However, not all MHC-I binders are T cell epitopes, and multiple studies have investigated what additional factors are important for determining the immunogenicity of a peptide. A recent study suggested that pMHC stability plays an important role in determining if a peptide can become a T cell epitope. Likewise, a T cell propensity model has been proposed for identifying MHC binding peptides with amino acid compositions favoring T cell receptor interactions. In this study, we investigate if improved accuracy for T cell epitope discovery can be achieved by integrating predictions for pMHC binding affinity, pMHC stability, and T cell propensity. We show that a weighted sum approach allows pMHC stability and T cell propensity predictions to enrich pMHC binding affinity predictions. The integrated model leads to a consistent and significant increase in predictive performance and we demonstrate how this can be utilized to decrease the experimental workload of epitope screens. The final method, NetTepi, is publically available at www.cbs.dtu.dk/services/NetTepi |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-08 |
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/11336/17886 Trolle, Thomas; Nielsen, Morten; NetTepi: an integrated method for the prediction of T-cell epitopes; Springer Verlag Berlín; Immunogenetics; 66; 7; 8-2014; 449-456 0093-7711 1432-1211 |
url |
http://hdl.handle.net/11336/17886 |
identifier_str_mv |
Trolle, Thomas; Nielsen, Morten; NetTepi: an integrated method for the prediction of T-cell epitopes; Springer Verlag Berlín; Immunogenetics; 66; 7; 8-2014; 449-456 0093-7711 1432-1211 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs00251-014-0779-0 info:eu-repo/semantics/altIdentifier/doi/10.1007/s00251-014-0779-0 |
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 Verlag Berlín |
publisher.none.fl_str_mv |
Springer Verlag Berlín |
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 |
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1842269341175775232 |
score |
13.13397 |