Immunoinformatics: Predicting Peptide–MHC Binding
- Autores
- Nielsen, Morten; Andreatta, Massimo; Peters, Bjoern; Buus, Søren
- Año de publicación
- 2020
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide?MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.
Fil: Nielsen, Morten. 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. Technical University of Denmark; Dinamarca
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; Argentina
Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Buus, Søren. Universidad de Copenhagen; Dinamarca - Materia
-
T cells
MHC
Antigen presentation - 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:11336/140643
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Immunoinformatics: Predicting Peptide–MHC BindingNielsen, MortenAndreatta, MassimoPeters, BjoernBuus, SørenT cellsMHCAntigen presentationhttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide?MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.Fil: Nielsen, Morten. 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. Technical University of Denmark; DinamarcaFil: 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: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Buus, Søren. Universidad de Copenhagen; DinamarcaAnnual Review2020-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/140643Nielsen, Morten; Andreatta, Massimo; Peters, Bjoern; Buus, Søren; Immunoinformatics: Predicting Peptide–MHC Binding; Annual Review; Annual Review of Biomedical Data Science; 3; 1; 7-2020; 191-2152574-3414CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.annualreviews.org/doi/10.1146/annurev-biodatasci-021920-100259info:eu-repo/semantics/altIdentifier/doi/10.1146/annurev-biodatasci-021920-100259info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:45:13Zoai:ri.conicet.gov.ar:11336/140643instacron: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 09:45:14.107CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Immunoinformatics: Predicting Peptide–MHC Binding |
title |
Immunoinformatics: Predicting Peptide–MHC Binding |
spellingShingle |
Immunoinformatics: Predicting Peptide–MHC Binding Nielsen, Morten T cells MHC Antigen presentation |
title_short |
Immunoinformatics: Predicting Peptide–MHC Binding |
title_full |
Immunoinformatics: Predicting Peptide–MHC Binding |
title_fullStr |
Immunoinformatics: Predicting Peptide–MHC Binding |
title_full_unstemmed |
Immunoinformatics: Predicting Peptide–MHC Binding |
title_sort |
Immunoinformatics: Predicting Peptide–MHC Binding |
dc.creator.none.fl_str_mv |
Nielsen, Morten Andreatta, Massimo Peters, Bjoern Buus, Søren |
author |
Nielsen, Morten |
author_facet |
Nielsen, Morten Andreatta, Massimo Peters, Bjoern Buus, Søren |
author_role |
author |
author2 |
Andreatta, Massimo Peters, Bjoern Buus, Søren |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
T cells MHC Antigen presentation |
topic |
T cells MHC Antigen presentation |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/3.3 https://purl.org/becyt/ford/3 |
dc.description.none.fl_txt_mv |
Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide?MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally. Fil: Nielsen, Morten. 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. Technical University of Denmark; Dinamarca 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; Argentina Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Buus, Søren. Universidad de Copenhagen; Dinamarca |
description |
Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide?MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-07 |
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/11336/140643 Nielsen, Morten; Andreatta, Massimo; Peters, Bjoern; Buus, Søren; Immunoinformatics: Predicting Peptide–MHC Binding; Annual Review; Annual Review of Biomedical Data Science; 3; 1; 7-2020; 191-215 2574-3414 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/140643 |
identifier_str_mv |
Nielsen, Morten; Andreatta, Massimo; Peters, Bjoern; Buus, Søren; Immunoinformatics: Predicting Peptide–MHC Binding; Annual Review; Annual Review of Biomedical Data Science; 3; 1; 7-2020; 191-215 2574-3414 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.annualreviews.org/doi/10.1146/annurev-biodatasci-021920-100259 info:eu-repo/semantics/altIdentifier/doi/10.1146/annurev-biodatasci-021920-100259 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Annual Review |
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Annual Review |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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