Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes
- Autores
- Costa, Juan Gabriel; Faccendini, Pablo Luis; Sferco, Silvano Juan; Lagier, Claudia Marina; Marcipar, Ivan Sergio
- Año de publicación
- 2013
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- This work deals with the use of predictors to identify useful B-cell linear epitopes to develop immunoassays. Experimental techniques to meet this goal are quite expensive and time consuming. Therefore, we tested 5 free, online prediction methods (AAPPred, ABCpred, BcePred, BepiPred and Antigenic) widely used for predicting linear epitopes, using the primary structure of the protein as the only input. We chose a set of 65 experimentally well documented epitopes obtained by the most reliable experimental techniques as our true positive set. To compare the quality of the predictor methods we used their positive predictive value (PPV), i.e. the proportion of the predicted epitopes that are true, experimentally confirmed epitopes, in relation to all the epitopes predicted. We conclude that AAPPred and ABCpred yield the best results, as compared with the other programs and with a random prediction procedure. Our results also indicate that considering the consensual epitopes predicted by several programs does not improve the PPV.
Fil: Costa, Juan Gabriel. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Tecnología Inmunológica; Argentina
Fil: Faccendini, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Química Rosario; Argentina
Fil: Sferco, Silvano Juan. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Departamento de Física; Argentina
Fil: Lagier, Claudia Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Química Rosario; Argentina
Fil: Marcipar, Ivan Sergio. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Tecnología Inmunológica; Argentina - Materia
-
Diagnostic
Epitope
Prediction
Inmunochemistry - 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:11336/6058
Ver los metadatos del registro completo
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Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell EpitopesCosta, Juan GabrielFaccendini, Pablo LuisSferco, Silvano JuanLagier, Claudia MarinaMarcipar, Ivan SergioDiagnosticEpitopePredictionInmunochemistryhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1This work deals with the use of predictors to identify useful B-cell linear epitopes to develop immunoassays. Experimental techniques to meet this goal are quite expensive and time consuming. Therefore, we tested 5 free, online prediction methods (AAPPred, ABCpred, BcePred, BepiPred and Antigenic) widely used for predicting linear epitopes, using the primary structure of the protein as the only input. We chose a set of 65 experimentally well documented epitopes obtained by the most reliable experimental techniques as our true positive set. To compare the quality of the predictor methods we used their positive predictive value (PPV), i.e. the proportion of the predicted epitopes that are true, experimentally confirmed epitopes, in relation to all the epitopes predicted. We conclude that AAPPred and ABCpred yield the best results, as compared with the other programs and with a random prediction procedure. Our results also indicate that considering the consensual epitopes predicted by several programs does not improve the PPV.Fil: Costa, Juan Gabriel. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Tecnología Inmunológica; ArgentinaFil: Faccendini, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Química Rosario; ArgentinaFil: Sferco, Silvano Juan. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Departamento de Física; ArgentinaFil: Lagier, Claudia Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Química Rosario; ArgentinaFil: Marcipar, Ivan Sergio. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Tecnología Inmunológica; ArgentinaBentham Science Publishers2013-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/mswordhttp://hdl.handle.net/11336/6058Costa, Juan Gabriel; Faccendini, Pablo Luis; Sferco, Silvano Juan; Lagier, Claudia Marina; Marcipar, Ivan Sergio; Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes; Bentham Science Publishers; Protein And Peptide Letters; 20; 6; 6-2013; 724-7300929-8665enginfo:eu-repo/semantics/altIdentifier/url/http://www.eurekaselect.com/109291/articleinfo:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/altIdentifier/doi/10.2174/0929866511320060011info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:45:46Zoai:ri.conicet.gov.ar:11336/6058instacron: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 09:45:46.923CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes |
title |
Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes |
spellingShingle |
Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes Costa, Juan Gabriel Diagnostic Epitope Prediction Inmunochemistry |
title_short |
Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes |
title_full |
Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes |
title_fullStr |
Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes |
title_full_unstemmed |
Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes |
title_sort |
Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes |
dc.creator.none.fl_str_mv |
Costa, Juan Gabriel Faccendini, Pablo Luis Sferco, Silvano Juan Lagier, Claudia Marina Marcipar, Ivan Sergio |
author |
Costa, Juan Gabriel |
author_facet |
Costa, Juan Gabriel Faccendini, Pablo Luis Sferco, Silvano Juan Lagier, Claudia Marina Marcipar, Ivan Sergio |
author_role |
author |
author2 |
Faccendini, Pablo Luis Sferco, Silvano Juan Lagier, Claudia Marina Marcipar, Ivan Sergio |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
Diagnostic Epitope Prediction Inmunochemistry |
topic |
Diagnostic Epitope Prediction Inmunochemistry |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.4 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
This work deals with the use of predictors to identify useful B-cell linear epitopes to develop immunoassays. Experimental techniques to meet this goal are quite expensive and time consuming. Therefore, we tested 5 free, online prediction methods (AAPPred, ABCpred, BcePred, BepiPred and Antigenic) widely used for predicting linear epitopes, using the primary structure of the protein as the only input. We chose a set of 65 experimentally well documented epitopes obtained by the most reliable experimental techniques as our true positive set. To compare the quality of the predictor methods we used their positive predictive value (PPV), i.e. the proportion of the predicted epitopes that are true, experimentally confirmed epitopes, in relation to all the epitopes predicted. We conclude that AAPPred and ABCpred yield the best results, as compared with the other programs and with a random prediction procedure. Our results also indicate that considering the consensual epitopes predicted by several programs does not improve the PPV. Fil: Costa, Juan Gabriel. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Tecnología Inmunológica; Argentina Fil: Faccendini, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Química Rosario; Argentina Fil: Sferco, Silvano Juan. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Departamento de Física; Argentina Fil: Lagier, Claudia Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Química Rosario; Argentina Fil: Marcipar, Ivan Sergio. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Tecnología Inmunológica; Argentina |
description |
This work deals with the use of predictors to identify useful B-cell linear epitopes to develop immunoassays. Experimental techniques to meet this goal are quite expensive and time consuming. Therefore, we tested 5 free, online prediction methods (AAPPred, ABCpred, BcePred, BepiPred and Antigenic) widely used for predicting linear epitopes, using the primary structure of the protein as the only input. We chose a set of 65 experimentally well documented epitopes obtained by the most reliable experimental techniques as our true positive set. To compare the quality of the predictor methods we used their positive predictive value (PPV), i.e. the proportion of the predicted epitopes that are true, experimentally confirmed epitopes, in relation to all the epitopes predicted. We conclude that AAPPred and ABCpred yield the best results, as compared with the other programs and with a random prediction procedure. Our results also indicate that considering the consensual epitopes predicted by several programs does not improve the PPV. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-06 |
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/11336/6058 Costa, Juan Gabriel; Faccendini, Pablo Luis; Sferco, Silvano Juan; Lagier, Claudia Marina; Marcipar, Ivan Sergio; Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes; Bentham Science Publishers; Protein And Peptide Letters; 20; 6; 6-2013; 724-730 0929-8665 |
url |
http://hdl.handle.net/11336/6058 |
identifier_str_mv |
Costa, Juan Gabriel; Faccendini, Pablo Luis; Sferco, Silvano Juan; Lagier, Claudia Marina; Marcipar, Ivan Sergio; Evaluation and Comparison of the Ability of Online Available Prediction Programs to Predict True Linear B-cell Epitopes; Bentham Science Publishers; Protein And Peptide Letters; 20; 6; 6-2013; 724-730 0929-8665 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://www.eurekaselect.com/109291/article info:eu-repo/semantics/altIdentifier/doi/ info:eu-repo/semantics/altIdentifier/doi/10.2174/0929866511320060011 |
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 application/msword |
dc.publisher.none.fl_str_mv |
Bentham Science Publishers |
publisher.none.fl_str_mv |
Bentham Science Publishers |
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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1844613431287611392 |
score |
13.070432 |