Abstract
BackgroundIdentification of a bacterial protein's subcellular localization (SCL) is important for genome annotation, function prediction and drug or vaccine target identification. Subcellular fractionation techniques combined with recent proteomics technology permits the identification of large numbers of proteins from distinct bacterial compartments. However, the fractionation of a complex structure like the cell into several subcellular compartments is not a trivial task. Contamination from other compartments may occur, and some proteins may reside in multiple localizations. New computational methods have been reported over the past few years that now permit much more accurate, genome-wide analysis of the SCL of protein sequences deduced from genomes. There is a need to compare such computational methods with laboratory proteomics approaches to identify the most effective current approach for genome-wide localization characterization and annotation.ResultsIn this study, ten subcellular proteome analyses of bacterial compartments were reviewed. PSORTb version 2.0 was used to computationally predict the localization of proteins reported in these publications, and these computational predictions were then compared to the localizations determined by the proteomics study. By using a combined approach, we were able to identify a number of contaminants and proteins with dual localizations, and were able to more accurately identify membrane subproteomes. Our results allowed us to estimate the precision level of laboratory subproteome studies and we show here that, on average, recent high-precision computational methods such as PSORTb now have a lower error rate than laboratory methods.ConclusionWe have performed the first focused comparison of genome-wide proteomic and computational methods for subcellular localization identification, and show that computational methods have now attained a level of precision that is exceeding that of high-throughput laboratory approaches. We note that analysis of all cellular fractions collectively is required to effectively provide localization information from laboratory studies, and we propose an overall approach to genome-wide subcellular localization characterization that capitalizes on the complementary nature of current laboratory and computational methods.
Highlights
Identification of a bacterial protein's subcellular localization (SCL) is important for genome annotation, function prediction and drug or vaccine target identification
Comparison of computational and subproteomic-based predictions of SCL for 405 proteins When computational SCL predictions by PSORTb v.2.0 were compared to the selected subproteomic studies from Gram-negative bacteria, 405 proteins were identified which met our selection criteria – the results of the analyses could be matched to specific GenBank records from the organism being studied
While the authors of the two studies mentioned above do not claim that their approaches identify all contaminants, we found that a robust and comprehensive method such as PSORTb outperforms single methods designed to analyze specific features, such as signal peptides or transmembrane helices
Summary
Identification of a bacterial protein's subcellular localization (SCL) is important for genome annotation, function prediction and drug or vaccine target identification. Several types of laboratory methods are frequently used to identify a protein's localization Techniques such as immunofluorescence and immunoelectron microscopy [4], PhoA protein fusions [5], fluorescent-protein tagging [6], and the Western/SDS-PAGE [7] analysis of subcellular fractions are often applied to the analysis of either single proteins or a small sets of proteins. Proteomics technologies have been developed which are capable of providing SCL information for a much larger number of proteins Techniques such as two-dimensional gel electrophoresis and mass spectrometry [8,9,10,11,12] have been frequently used to analyze localization for a variety of bacterial genomes, including Pseudomonas aeruginosa [13] and Bacillus sp. Genome-scale techniques are rapid, cost-effective, and capable of returning results for hundreds or even thousands of proteins in a single analysis
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