title: Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv.
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.
authors.affiliation: Fundación Instituto de Inmunología de Colombia - FIDIC, Bogotá, Colombia.
authors.forename: Carolina
authors.initials: C
authors.lastname: Vizcaíno
authors.forename: Daniel
authors.initials: D
authors.lastname: Restrepo-Montoya
authors.forename: Diana
authors.initials: D
authors.lastname: Rodríguez
authors.forename: Luis F
authors.initials: LF
authors.lastname: Niño
authors.forename: Marisol
authors.initials: M
authors.lastname: Ocampo
authors.forename: Magnolia
authors.initials: M
authors.lastname: Vanegas
authors.forename: María T
authors.initials: MT
authors.lastname: Reguero
authors.forename: Nora L
authors.initials: NL
authors.lastname: Martínez
authors.forename: Manuel E
authors.initials: ME
authors.lastname: Patarroyo
authors.forename: Manuel A
authors.initials: MA
authors.lastname: Patarroyo
mesh_terms: 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
chemical_list: D000907:Antibodies, Bacterial; D001425:Bacterial Outer Membrane Proteins; D018985:Epitopes, B-Lymphocyte; D010455:Peptides