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 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.
Vizcaíno
Carolina
C
Fundación Instituto de Inmunología de Colombia - FIDIC, Bogotá, Colombia.
Restrepo-Montoya
Daniel
D
Rodríguez
Diana
D
Niño
Luis F
LF
Ocampo
Marisol
M
Vanegas
Magnolia
M
Reguero
María T
MT
Martínez
Nora L
NL
Patarroyo
Manuel E
ME
Patarroyo
Manuel A
MA
D000818:Animals; D000907:Antibodies, Bacterial / Q000737:chemistry / Q000378:metabolism; D001185:Artificial Intelligence; D001425:Bacterial Outer Membrane Proteins / Q000737:chemistry / Q000378:metabolism*; D002458:Cell Fractionation; D019295:Computational Biology / Q000379:methods*; D004591:Electrophoresis, Polyacrylamide Gel; D018985:Epitopes, B-Lymphocyte / Q000276:immunology / Q000378:metabolism; D004926:Escherichia coli / Q000378:metabolism; D015151:Immunoblotting; D016253:Microscopy, Immunoelectron; D015233:Models, Statistical; D020102:Mycobacterium smegmatis / Q000378:metabolism; D009169:Mycobacterium tuberculosis / Q000737:chemistry / Q000378:metabolism*; D010455:Peptides / Q000276:immunology / Q000378:metabolism; D011817:Rabbits; D013010:Sonication; D013347:Subcellular Fractions / Q000378:metabolism
D000907:Antibodies, Bacterial; D001425:Bacterial Outer Membrane Proteins; D018985:Epitopes, B-Lymphocyte; D010455:Peptides