Mesh : Alkaline Phosphatase / genetics Amino Acid Motifs / genetics Amino Acid Sequence / genetics Amino Acid Transport Systems / genetics metabolism Arginine / metabolism Bacterial Outer Membrane Proteins Bacterial Proteins / chemistry genetics Cloning, Molecular / methods Computational Biology / methods Consensus Sequence / genetics Endopeptidases / metabolism Genome, Bacterial Lipoproteins / genetics Molecular Sequence Data Predictive Value of Tests Protein Sorting Signals / genetics Protein Structure, Tertiary / genetics Pseudomonas aeruginosa / genetics Recombinant Fusion Proteins / genetics Substrate Specificity / genetics Transcription Initiation Site

来  源:   DOI:10.1101/gr.3257305   PDF(Sci-hub)   PDF(Pubmed)

Abstract:
The Gram-negative pathogen Pseudomonas aeruginosa encodes multiple protein export systems, the substrates of which contain export signals such as N-terminal signal peptides. Here we report the first genome-wide computational and laboratory screen for N-terminal signal peptides in this important opportunistic pathogen. The computational identification of signal peptides was based on a consensus between multiple predictive tools and showed that 38% of the P. aeruginosa PAO1 proteome was predicted to encode exported proteins, most of which utilize cleavable type I signal peptides or uncleavable transmembrane helices. In addition, known and novel lipoproteins (type II), twin arginine transporter (TAT), and prepilin peptidase substrates (type IV) were also identified. A laboratory-based screen using the alkaline phosphatase (PhoA) fusion method was then used to test our predictions. In total, 310 nonredundant PhoA fusions were successfully identified, 296 of which possess a predicted export signal. Analysis of the PhoA fusion proteins lacking an export signal revealed that three proteins have alternate translation start sites that encode signal peptides, two proteins may use an unknown export signal, and the remaining nine proteins are likely cytoplasmic proteins and represent false positives associated with the PhoA screen. Our approach to identify exported proteins illustrates how computational and laboratory-based methods are complementary, where computational analyses provide a large number of accurate predictions while laboratory methods both confirm predictions and reveal unique cases meriting further analysis.
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