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
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/6058
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling 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 Técnicas2025-10-22T11:14:32Zoai: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:34982025-10-22 11:14:32.816CONICET 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
_version_ 1846781567111790592
score 12.982451

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