An assessment on epitope prediction methods for protozoa genomes.
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFOP |
Texto Completo: | http://www.repositorio.ufop.br/handle/123456789/7258 https://doi.org/10.1186/1471-2105-13-309 |
Resumo: | Background: Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a) evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve) performance and a threshold dependent method that employs a confusion matrix; b) integrating data from experimentally validated and in silico predicted epitopes; and c) integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes. Results: A database-driven epitope prediction method was developed with built-in functions that were capable of: a) removing experimental data redundancy; b) parsing algorithms predictions and storage experimental validated and predict data; and c) evaluating algorithm performances. Results show that a better performance is achieved when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the combined prediction of AAP12 and BCPred12 reached an AUC value of 0.77. For T CD8+ epitope predictors, the combined prediction of NetCTL and NetMHC reached an AUC value of 0.64. Finally, regarding the subcellular localization prediction, the best performance is achieved when the combined prediction of Sigcleave, TargetP and WoLF PSORT is used. Conclusions: Our study indicates that the combination of B cells epitope predictors is the best tool for predicting epitopes on protozoan parasites proteins. Regarding subcellular localization, the best result was obtained when the three algorithms predictions were combined. The developed pipeline is available upon request to authors. |
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An assessment on epitope prediction methods for protozoa genomes.Background: Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a) evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve) performance and a threshold dependent method that employs a confusion matrix; b) integrating data from experimentally validated and in silico predicted epitopes; and c) integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes. Results: A database-driven epitope prediction method was developed with built-in functions that were capable of: a) removing experimental data redundancy; b) parsing algorithms predictions and storage experimental validated and predict data; and c) evaluating algorithm performances. Results show that a better performance is achieved when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the combined prediction of AAP12 and BCPred12 reached an AUC value of 0.77. For T CD8+ epitope predictors, the combined prediction of NetCTL and NetMHC reached an AUC value of 0.64. Finally, regarding the subcellular localization prediction, the best performance is achieved when the combined prediction of Sigcleave, TargetP and WoLF PSORT is used. Conclusions: Our study indicates that the combination of B cells epitope predictors is the best tool for predicting epitopes on protozoan parasites proteins. Regarding subcellular localization, the best result was obtained when the three algorithms predictions were combined. The developed pipeline is available upon request to authors.2017-02-13T16:10:42Z2017-02-13T16:10:42Z2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfRESENDE, D. M. et al. An assessment on epitope prediction methods for protozoa genomes. Bmc Bioinformatics, v. 13, p.309-322, 2012. Disponível em: <https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-309>. Acesso em: 10 out. 2016.1471-2105http://www.repositorio.ufop.br/handle/123456789/7258https://doi.org/10.1186/1471-2105-13-309This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Fonte: o próprio artigo.info:eu-repo/semantics/openAccessResende, Daniela de MeloRezende, Antonio MauroOliveira, Nesley Jesus Daher deBatista, Izabella Cristina AndradeOliveira, Rodrigo Corrêa deReis, Alexandre BarbosaRuiz, Jeronimo Conceiçãoengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOP2019-10-24T16:10:54Zoai:repositorio.ufop.br:123456789/7258Repositório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332019-10-24T16:10:54Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false |
dc.title.none.fl_str_mv |
An assessment on epitope prediction methods for protozoa genomes. |
title |
An assessment on epitope prediction methods for protozoa genomes. |
spellingShingle |
An assessment on epitope prediction methods for protozoa genomes. Resende, Daniela de Melo |
title_short |
An assessment on epitope prediction methods for protozoa genomes. |
title_full |
An assessment on epitope prediction methods for protozoa genomes. |
title_fullStr |
An assessment on epitope prediction methods for protozoa genomes. |
title_full_unstemmed |
An assessment on epitope prediction methods for protozoa genomes. |
title_sort |
An assessment on epitope prediction methods for protozoa genomes. |
author |
Resende, Daniela de Melo |
author_facet |
Resende, Daniela de Melo Rezende, Antonio Mauro Oliveira, Nesley Jesus Daher de Batista, Izabella Cristina Andrade Oliveira, Rodrigo Corrêa de Reis, Alexandre Barbosa Ruiz, Jeronimo Conceição |
author_role |
author |
author2 |
Rezende, Antonio Mauro Oliveira, Nesley Jesus Daher de Batista, Izabella Cristina Andrade Oliveira, Rodrigo Corrêa de Reis, Alexandre Barbosa Ruiz, Jeronimo Conceição |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Resende, Daniela de Melo Rezende, Antonio Mauro Oliveira, Nesley Jesus Daher de Batista, Izabella Cristina Andrade Oliveira, Rodrigo Corrêa de Reis, Alexandre Barbosa Ruiz, Jeronimo Conceição |
description |
Background: Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a) evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve) performance and a threshold dependent method that employs a confusion matrix; b) integrating data from experimentally validated and in silico predicted epitopes; and c) integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes. Results: A database-driven epitope prediction method was developed with built-in functions that were capable of: a) removing experimental data redundancy; b) parsing algorithms predictions and storage experimental validated and predict data; and c) evaluating algorithm performances. Results show that a better performance is achieved when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the combined prediction of AAP12 and BCPred12 reached an AUC value of 0.77. For T CD8+ epitope predictors, the combined prediction of NetCTL and NetMHC reached an AUC value of 0.64. Finally, regarding the subcellular localization prediction, the best performance is achieved when the combined prediction of Sigcleave, TargetP and WoLF PSORT is used. Conclusions: Our study indicates that the combination of B cells epitope predictors is the best tool for predicting epitopes on protozoan parasites proteins. Regarding subcellular localization, the best result was obtained when the three algorithms predictions were combined. The developed pipeline is available upon request to authors. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012 2017-02-13T16:10:42Z 2017-02-13T16:10:42Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
RESENDE, D. M. et al. An assessment on epitope prediction methods for protozoa genomes. Bmc Bioinformatics, v. 13, p.309-322, 2012. Disponível em: <https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-309>. Acesso em: 10 out. 2016. 1471-2105 http://www.repositorio.ufop.br/handle/123456789/7258 https://doi.org/10.1186/1471-2105-13-309 |
identifier_str_mv |
RESENDE, D. M. et al. An assessment on epitope prediction methods for protozoa genomes. Bmc Bioinformatics, v. 13, p.309-322, 2012. Disponível em: <https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-309>. Acesso em: 10 out. 2016. 1471-2105 |
url |
http://www.repositorio.ufop.br/handle/123456789/7258 https://doi.org/10.1186/1471-2105-13-309 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFOP instname:Universidade Federal de Ouro Preto (UFOP) instacron:UFOP |
instname_str |
Universidade Federal de Ouro Preto (UFOP) |
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UFOP |
institution |
UFOP |
reponame_str |
Repositório Institucional da UFOP |
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Repositório Institucional da UFOP |
repository.name.fl_str_mv |
Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP) |
repository.mail.fl_str_mv |
repositorio@ufop.edu.br |
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1813002805576728576 |