Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv

The 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 tuberc...

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Autores Principales: Vizcaíno, Carolina, Restrepo-Montoya, Daniel, Rodríguez, Diana, Niño, Luis F., Ocampo, Marisol, Vanegas, Magnolia, Reguero, María T., Martínez, Nora L., Patarroyo, Manuel E., Patarroyo, Manuel A.
Formato: Artículo (Article)
Lenguaje:Inglés (English)
Publicado: 2010
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Acceso en línea:http://repository.urosario.edu.co/handle/10336/18754
id ir-10336-18754
recordtype dspace
spelling ir-10336-187542019-09-19T12:38:03Z Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv Vizcaíno, Carolina Restrepo-Montoya, Daniel Rodríguez, Diana Niño, Luis F. Ocampo, Marisol Vanegas, Magnolia Reguero, María T. Martínez, Nora L. Patarroyo, Manuel E. Patarroyo, Manuel A. Bacterial Protein Cytoplasm Protein Membrane Protein Peptide Vaccine Protein Rv178 Protein Rv361 Protein Rv43C Protein Rv835 Protein Rv122 Protein Rv363 Unclassified Drug Bacterium Antibody Epitope Outer Membrane Protein Peptide Animal Experiment Article Bacterial Genome Bacterial Strain Cell Fractionation Computer Prediction Controlled Study Cytoplasm Drug Identification Machine Learning Mathematical Computing Membrane Structure Mycobacterium Tuberculosis Nonhuman Protein Localization Protein Secretion Vaccine Production Animal Artificial Intelligence Biology Chemistry Escherichia Coli Immunoblotting Immunoelectron Microscopy Immunology Metabolism Methodology Mycobacterium Smegmatis Polyacrylamide Gel Electrophoresis Rabbit Statistical Model Ultrasound Mycobacterium Tuberculosis Animals Antibodies, Bacterial Artificial Intelligence Bacterial Outer Membrane Proteins Cell Fractionation Computational Biology Electrophoresis, Polyacrylamide Gel Epitopes, B-Lymphocyte Escherichia Coli Immunoblotting Microscopy, Immunoelectron Models, Statistical Mycobacterium Smegmatis Mycobacterium Tuberculosis Peptides Rabbits Sonication Subcellular Fractions Mycobacterium tuberculosis Mycobacterium Immunoblotting The 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. 2010 2018-11-29T15:13:09Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion ISSN 1553-734X http://repository.urosario.edu.co/handle/10336/18754 eng info:eu-repo/semantics/openAccess application/pdf (2009) Global Tuberculosis Control: Surveillance, Planning, Financing, , WHO, World Health Organization. Genova: WHO, World Health Organization
institution EdocUR - Universidad del Rosario
collection DSpace
language Inglés (English)
topic Bacterial Protein
Cytoplasm Protein
Membrane Protein
Peptide Vaccine
Protein Rv178
Protein Rv361
Protein Rv43C
Protein Rv835
Protein Rv122
Protein Rv363
Unclassified Drug
Bacterium Antibody
Epitope
Outer Membrane Protein
Peptide
Animal Experiment
Article
Bacterial Genome
Bacterial Strain
Cell Fractionation
Computer Prediction
Controlled Study
Cytoplasm
Drug Identification
Machine Learning
Mathematical Computing
Membrane Structure
Mycobacterium Tuberculosis
Nonhuman
Protein Localization
Protein Secretion
Vaccine Production
Animal
Artificial Intelligence
Biology
Chemistry
Escherichia Coli
Immunoblotting
Immunoelectron Microscopy
Immunology
Metabolism
Methodology
Mycobacterium Smegmatis
Polyacrylamide Gel Electrophoresis
Rabbit
Statistical Model
Ultrasound
Mycobacterium Tuberculosis
Animals
Antibodies, Bacterial
Artificial Intelligence
Bacterial Outer Membrane Proteins
Cell Fractionation
Computational Biology
Electrophoresis, Polyacrylamide Gel
Epitopes, B-Lymphocyte
Escherichia Coli
Immunoblotting
Microscopy, Immunoelectron
Models, Statistical
Mycobacterium Smegmatis
Mycobacterium Tuberculosis
Peptides
Rabbits
Sonication
Subcellular Fractions
Mycobacterium tuberculosis
Mycobacterium
Immunoblotting
spellingShingle Bacterial Protein
Cytoplasm Protein
Membrane Protein
Peptide Vaccine
Protein Rv178
Protein Rv361
Protein Rv43C
Protein Rv835
Protein Rv122
Protein Rv363
Unclassified Drug
Bacterium Antibody
Epitope
Outer Membrane Protein
Peptide
Animal Experiment
Article
Bacterial Genome
Bacterial Strain
Cell Fractionation
Computer Prediction
Controlled Study
Cytoplasm
Drug Identification
Machine Learning
Mathematical Computing
Membrane Structure
Mycobacterium Tuberculosis
Nonhuman
Protein Localization
Protein Secretion
Vaccine Production
Animal
Artificial Intelligence
Biology
Chemistry
Escherichia Coli
Immunoblotting
Immunoelectron Microscopy
Immunology
Metabolism
Methodology
Mycobacterium Smegmatis
Polyacrylamide Gel Electrophoresis
Rabbit
Statistical Model
Ultrasound
Mycobacterium Tuberculosis
Animals
Antibodies, Bacterial
Artificial Intelligence
Bacterial Outer Membrane Proteins
Cell Fractionation
Computational Biology
Electrophoresis, Polyacrylamide Gel
Epitopes, B-Lymphocyte
Escherichia Coli
Immunoblotting
Microscopy, Immunoelectron
Models, Statistical
Mycobacterium Smegmatis
Mycobacterium Tuberculosis
Peptides
Rabbits
Sonication
Subcellular Fractions
Mycobacterium tuberculosis
Mycobacterium
Immunoblotting
Vizcaíno, Carolina
Restrepo-Montoya, Daniel
Rodríguez, Diana
Niño, Luis F.
Ocampo, Marisol
Vanegas, Magnolia
Reguero, María T.
Martínez, Nora L.
Patarroyo, Manuel E.
Patarroyo, Manuel A.
Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv
description The 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.
format Artículo (Article)
author Vizcaíno, Carolina
Restrepo-Montoya, Daniel
Rodríguez, Diana
Niño, Luis F.
Ocampo, Marisol
Vanegas, Magnolia
Reguero, María T.
Martínez, Nora L.
Patarroyo, Manuel E.
Patarroyo, Manuel A.
author_facet Vizcaíno, Carolina
Restrepo-Montoya, Daniel
Rodríguez, Diana
Niño, Luis F.
Ocampo, Marisol
Vanegas, Magnolia
Reguero, María T.
Martínez, Nora L.
Patarroyo, Manuel E.
Patarroyo, Manuel A.
author_sort Vizcaíno, Carolina
title Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv
title_short Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv
title_full Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv
title_fullStr Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv
title_full_unstemmed Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv
title_sort computational prediction and experimental assessment of secreted/surface proteins from mycobacterium tuberculosis h37rv
publishDate 2010
url http://repository.urosario.edu.co/handle/10336/18754
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score 10,762304