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

Carolina Vizcaíno, Daniel Restrepo-Montoya, Diana Rodríguez, Luis F. Niño, Marisol Ocampo, Magnolia Vanegas, María T. Reguero, Nora L. Martínez, Manuel E. Patarroyo, Manuel A. Patarroyo

Resultado de la investigación: Contribución a RevistaArtículo

28 Citas (Scopus)

Resumen

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.
Idioma originalEnglish (US)
Páginas (desde-hasta)1-14
Número de páginas14
PublicaciónPLoS Computational Biology
DOI
EstadoPublished - sep 7 2010

Huella dactilar

Tuberculosis
tuberculosis
surface proteins
Mycobacterium tuberculosis
Membrane Proteins
Proteins
Protein
protein
prediction
Immunoelectron Microscopy
Prediction
microscopy
fractionation
Vaccines
Vaccine
vaccine
Fractionation
proteins
artificial intelligence
Secretory Pathway

Citar esto

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. En: PLoS Computational Biology. 2010 ; pp. 1-14.
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abstract = "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. {\circledC} 2010 Vizca{\'i}no et al.",
author = "Carolina Vizca{\'i}no and Daniel Restrepo-Montoya and Diana Rodr{\'i}guez and Ni{\~n}o, {Luis F.} and Marisol Ocampo and Magnolia Vanegas and Reguero, {Mar{\'i}a T.} and Mart{\'i}nez, {Nora L.} and Patarroyo, {Manuel E.} and Patarroyo, {Manuel A.}",
year = "2010",
month = "9",
day = "7",
doi = "10.1371/journal.pcbi.1000824",
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Vizcaíno, C, Restrepo-Montoya, D, Rodríguez, D, Niño, LF, Ocampo, M, Vanegas, M, Reguero, MT, Martínez, NL, Patarroyo, ME & Patarroyo, MA 2010, 'Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv', PLoS Computational Biology, pp. 1-14. https://doi.org/10.1371/journal.pcbi.1000824

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.

En: PLoS Computational Biology, 07.09.2010, p. 1-14.

Resultado de la investigación: Contribución a RevistaArtículo

TY - JOUR

T1 - Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv

AU - Vizcaíno, Carolina

AU - Restrepo-Montoya, Daniel

AU - Rodríguez, Diana

AU - Niño, Luis F.

AU - Ocampo, Marisol

AU - Vanegas, Magnolia

AU - Reguero, María T.

AU - Martínez, Nora L.

AU - Patarroyo, Manuel E.

AU - Patarroyo, Manuel A.

PY - 2010/9/7

Y1 - 2010/9/7

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pcbi.1000824

DO - 10.1371/journal.pcbi.1000824

M3 - Article

SP - 1

EP - 14

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

ER -