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dc.creatorVizcaíno, Carolina 
dc.creatorRestrepo-Montoya, Daniel 
dc.creatorRodríguez, Diana 
dc.creatorNiño, Luis F. 
dc.creatorOcampo, Marisol 
dc.creatorVanegas, Magnolia 
dc.creatorReguero, María T. 
dc.creatorMartínez, Nora L. 
dc.creatorPatarroyo, Manuel E. 
dc.creatorPatarroyo, Manuel A. 
dc.date.accessioned2018-11-29T15:13:09Z
dc.date.available2018-11-29T15:13:09Z
dc.date.created2010
dc.date.issued2010 
dc.identifier.issnISSN 1553-734X
dc.identifier.urihttp://repository.urosario.edu.co/handle/10336/18754
dc.descriptionThe mycobacterial cell envelope has been implicated in the pathogenicity of tuberculosis and therefore has been a prime target for the identification and characterization of surface proteins with potential application in drug and vaccine development. In this study, the genome of Mycobacterium tuberculosis H37Rv was screened using Machine Learning tools that included feature-based predictors, general localizers and transmembrane topology predictors to identify proteins that are potentially secreted to the surface of M. tuberculosis, or to the extracellular milieu through different secretory pathways. The subcellular localization of a set of 8 hypothetically secreted/surface candidate proteins was experimentally assessed by cellular fractionation and immunoelectron microscopy (IEM) to determine the reliability of the computational methodology proposed here, using 4 secreted/surface proteins with experimental confirmation as positive controls and 2 cytoplasmic proteins as negative controls. Subcellular fractionation and IEM studies provided evidence that the candidate proteins Rv0403c, Rv3630, Rv1022, Rv0835, Rv0361 and Rv0178 are secreted either to the mycobacterial surface or to the extracellular milieu. Surface localization was also confirmed for the positive controls, whereas negative controls were located on the cytoplasm. Based on statistical learning methods, we obtained computational subcellular localization predictions that were experimentally assessed and allowed us to construct a computational protocol with experimental support that allowed us to identify a new set of secreted/surface proteins as potential vaccine candidates. © 2010 Vizcaíno et al.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.relation.ispartofPLoS Computational Biology, ISSN: 1553-734X, Vol. 6/No. 6 (2010) pp. 1-14
dc.relation.urihttps://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000824&type=printable
dc.rights.uri
dc.subjectBacterial Protein
dc.subjectCytoplasm Protein
dc.subjectMembrane Protein
dc.subjectPeptide Vaccine
dc.subjectProtein Rv178
dc.subjectProtein Rv361
dc.subjectProtein Rv43C
dc.subjectProtein Rv835
dc.subjectProtein Rv122
dc.subjectProtein Rv363
dc.subjectUnclassified Drug
dc.subjectBacterium Antibody
dc.subjectEpitope
dc.subjectOuter Membrane Protein
dc.subjectPeptide
dc.subjectAnimal Experiment
dc.subjectBacterial Genome
dc.subjectBacterial Strain
dc.subjectCell Fractionation
dc.subjectComputer Prediction
dc.subjectControlled Study
dc.subjectCytoplasm
dc.subjectDrug Identification
dc.subjectMachine Learning
dc.subjectMathematical Computing
dc.subjectMembrane Structure
dc.subjectMycobacterium Tuberculosis
dc.subjectNonhuman
dc.subjectProtein Localization
dc.subjectProtein Secretion
dc.subjectVaccine Production
dc.subjectAnimal
dc.subjectArtificial Intelligence
dc.subjectBiology
dc.subjectChemistry
dc.subjectEscherichia Coli
dc.subjectImmunoblotting
dc.subjectImmunoelectron Microscopy
dc.subjectImmunology
dc.subjectMetabolism
dc.subjectMethodology
dc.subjectMycobacterium Smegmatis
dc.subjectPolyacrylamide Gel Electrophoresis
dc.subjectRabbit
dc.subjectStatistical Model
dc.subjectUltrasound
dc.subjectMycobacterium Tuberculosis
dc.subjectAnimals
dc.subjectAntibodies, Bacterial
dc.subjectArtificial Intelligence
dc.subjectBacterial Outer Membrane Proteins
dc.subjectCell Fractionation
dc.subjectComputational Biology
dc.subjectElectrophoresis, Polyacrylamide Gel
dc.subjectEpitopes, B-Lymphocyte
dc.subjectEscherichia Coli
dc.subjectImmunoblotting
dc.subjectMicroscopy, Immunoelectron
dc.subjectModels, Statistical
dc.subjectMycobacterium Smegmatis
dc.subjectMycobacterium Tuberculosis
dc.subjectPeptides
dc.subjectRabbits
dc.subjectSonication
dc.subjectSubcellular Fractions
dc.subject.lembMycobacterium tuberculosis
dc.subject.lembMycobacterium
dc.subject.lembImmunoblotting
dc.titleComputational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv
dc.typearticle
dc.subject.keywordArticle
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.type.spaArtículo
dc.rights.accesoAbierto (Texto Completo)
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.source.bibliographicCitation(2009) Global Tuberculosis Control: Surveillance, Planning, Financing, , WHO, World Health Organization. Genova: WHO, World Health Organization
dc.rights.cchttps://creativecommons.org/licenses/by/4.0/
dc.creator.googleVizcaíno, Carolina
dc.creator.googleRestrepo-Montoya, Daniel
dc.creator.googleRodríguez, Diana
dc.creator.googleNiño, Luis F.
dc.creator.googleOcampo, Marisol
dc.creator.googleVanegas, Magnolia
dc.creator.googleReguero, María T.
dc.creator.googleMartínez, Nora L.
dc.creator.googlePatarroyo, Manuel E.
dc.creator.googlePatarroyo, Manuel A.


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