ArticlePLoS computational biology2010
Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 51 citations in OpenAlex.
- B-vac a robust software package for bacterial vaccine design.Scientific reports · 2025Article
- Mycobacterium tuberculosis Rv1324 Protein Contributes to Mycobacterial Persistence and Causes Pathological Lung Injury in Mice by Inducing Ferroptosis.Microbiology spectrum · 2023Article
- Single cell analysis of host response to helminth infection reveals the clonal breadth, heterogeneity, and tissue-specific programming of the responding CD4+ T cell repertoire.PLoS pathogens · 2021Article
- Comparative Secretome Analyses ofFrontiers in veterinary science · 2021Article
- Rv3091, An Extracellular Patatin-Like Phospholipase inFrontiers in microbiology · 2020Article
- A genomic analysis of Mycobacterium immunogenum strain CD11_6 and its potential role in the activation of T cells against Mycobacterium tuberculosis.BMC microbiology · 2019Article
- Specific Binding Peptides from Rv3632: A Strategy for BlockingBioMed research international · 2019Article
- Extracellular Sphingomyelinase Rv0888 ofFrontiers in immunology · 2018Article
- Comprehensive profiling of functional attributes, virulence potential and evolutionary dynamics in mycobacterial secretomes.World journal of microbiology & biotechnology · 2017Article
- Secretome Prediction of TwoFrontiers in microbiology · 2017Article
- Identification of Secretory Proteins in Mycobacterium tuberculosis Using Pseudo Amino Acid Composition.BioMed research international · 2016Article
- Identification and Characterization of Lipase Activity and Immunogenicity of LipL from Mycobacterium tuberculosis.PloS one · 2015Article
- Article
- Specific interaction between Mycobacterium tuberculosis lipoprotein-derived peptides and target cells inhibits mycobacterial entry in vitro.Chemical biology & drug design · 2014Article
- Article
- Computational prediction and experimental assessment of an HLA-A*0201-restricted cytotoxic T lymphocyte epitope from neutral endopeptidase.Immunologic research · 2012Article
- NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins.BMC bioinformatics · 2011Article
- Effective--a database of predicted secreted bacterial proteins.Nucleic acids research · 2011Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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