Ítem
Acceso Abierto

Validating subcellular localization prediction tools with mycobacterial proteins

Mostrar el registro sencillo de la publicación
dc.creatorRestrepo-Montoya, Daniel
dc.creatorVizcaíno, Carolina
dc.creatorNiño, Luis F.
dc.creatorOcampo, Marisol
dc.creatorPatarroyo, Manuel-Elkin
dc.creatorPatarroyo, Manuel A.
dc.creator.googleRestrepo-Montoya, Danielspa
dc.creator.googleVizcaíno, Carolinaspa
dc.creator.googleNiño, Luis Fspa
dc.creator.googleOcampo, Marisolspa
dc.creator.googlePatarroyo, Manuel Espa
dc.creator.googlePatarroyo, Manuel Aspa
dc.date.accessioned2020-05-08T03:41:27Z
dc.date.available2020-05-08T03:41:27Z
dc.date.created2009
dc.date.issued2009
dc.description.abstractBackground: The computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins. Results: A final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out. Conclusion: Both subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model. © 2009 Restrepo-Montoya et al; licensee BioMed Central Ltd.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1186/1471-2105-10-134
dc.identifier.issn1471-2105
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/21913
dc.language.isoengspa
dc.relation.citationTitleBMC Bioinformatics
dc.relation.citationVolumeVol. 10
dc.relation.ispartofBMC Bioinformatics, ISSN: 1471-2105 Vol. 10, (2009)spa
dc.relation.urihttps://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-10-134spa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subject.ddcMicrobiologíaspa
dc.subject.keywordComputational predictionsspa
dc.subject.keywordComputational toolsspa
dc.subject.keywordPredictive performancespa
dc.subject.keywordBacterial Proteinsspa
dc.subject.keywordPredictive performancespa
dc.subject.keywordMycobacteriumspa
dc.subject.keywordBacteria (microorganisms)spa
dc.titleValidating subcellular localization prediction tools with mycobacterial proteinsspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
Archivos
Bloque original
Mostrando1 - 1 de 1
Miniatura
Nombre:
Validating_subcellular_localization.pdf
Tamaño:
553.86 KB
Formato:
Adobe Portable Document Format
Descripción:
Descargar
Colecciones
Artículos