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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/49512
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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 Técnicas2025-09-03T10:07:16Zoai: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:34982025-09-03 10:07:17.157CONICET 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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1842269997229932544 |
score |
13.13397 |