BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes

Autores
Jespersen, Martin Closter; Peters, Bjoern; Nielsen, Morten; Marcatili, Paolo
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.
Fil: Jespersen, Martin Closter. Technical University of Denmark; Dinamarca
Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos
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: Marcatili, Paolo. Technical University of Denmark; Dinamarca
Materia
B cell
Epitopo
Prediction
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/49512
Acceder
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network_name_str CONICET Digital (CONICET)
spelling BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopesJespersen, Martin ClosterPeters, BjoernNielsen, MortenMarcatili, PaoloB cellEpitopoPredictionhttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.Fil: Jespersen, Martin Closter. Technical University of Denmark; DinamarcaFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: 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: Marcatili, Paolo. Technical University of Denmark; DinamarcaOxford University Press2017-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/49512Jespersen, Martin Closter; Peters, Bjoern; Nielsen, Morten; Marcatili, Paolo; BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes; Oxford University Press; Nucleic Acids Research; 45; W1; 7-2017; W24-W290305-10481362-4962CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1093/nar/gkx346info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/nar/article/45/W1/W24/3787843info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:32:56Zoai:ri.conicet.gov.ar:11336/49512instacron: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:34982025-09-29 10:32:56.694CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes
title BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes
spellingShingle BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes
Jespersen, Martin Closter
B cell
Epitopo
Prediction
title_short BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes
title_full BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes
title_fullStr BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes
title_full_unstemmed BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes
title_sort BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes
dc.creator.none.fl_str_mv Jespersen, Martin Closter
Peters, Bjoern
Nielsen, Morten
Marcatili, Paolo
author Jespersen, Martin Closter
author_facet Jespersen, Martin Closter
Peters, Bjoern
Nielsen, Morten
Marcatili, Paolo
author_role author
author2 Peters, Bjoern
Nielsen, Morten
Marcatili, Paolo
author2_role author
author
author
dc.subject.none.fl_str_mv B cell
Epitopo
Prediction
topic B cell
Epitopo
Prediction
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.
Fil: Jespersen, Martin Closter. Technical University of Denmark; Dinamarca
Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos
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: Marcatili, Paolo. Technical University of Denmark; Dinamarca
description Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.
publishDate 2017
dc.date.none.fl_str_mv 2017-07
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/11336/49512
Jespersen, Martin Closter; Peters, Bjoern; Nielsen, Morten; Marcatili, Paolo; BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes; Oxford University Press; Nucleic Acids Research; 45; W1; 7-2017; W24-W29
0305-1048
1362-4962
CONICET Digital
CONICET
url http://hdl.handle.net/11336/49512
identifier_str_mv Jespersen, Martin Closter; Peters, Bjoern; Nielsen, Morten; Marcatili, Paolo; BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes; Oxford University Press; Nucleic Acids Research; 45; W1; 7-2017; W24-W29
0305-1048
1362-4962
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/nar/gkx346
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/nar/article/45/W1/W24/3787843
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 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
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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score 13.069144

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