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Article abstract


REFERENCE (prediction of non-classical secretion in bacteria)

Non-classical protein secretion in bacteria
J. D. Bendtsen, L. Kiemer, A. Fausbøll and S. Brunak.
BMC Microbiology 2005, 5:58


ABSTRACT

Background: We present an overview of bacterial non-classical secretion and a prediction method for identification of proteins following signal peptide independent secretion pathways. We have compiled a list of proteins found extracellularly despite the absence of a signal peptide. Some of these proteins also have known roles in the cytoplasm, which means they could be so-called ``moon-lightning'' proteins having more than one function.

Methods: A thorough literature search was conducted to compile a list of currently known bacterial non-classically secreted proteins. Pattern finding methods were applied to the sequences in order to identify putative signal sequences or motifs responsible for their secretion. Finally, artificial neural networks were used to construct protein feature based methods for identification of non-classically secreted proteins in both Gram-positive and Gram-negative bacteria.

Results: We have found no signal or motif characteristic to any majority of the proteins in the compiled list of non-classically secreted proteins, and conclude that these proteins, indeed, seem to be secreted in a novel fashion. However, we also show that the apparently non-classically secreted proteins are still distinguished from cellular proteins by properties such as amino acid composition, secondary structure and disordered regions. Specifically, prediction of disorder reveals that bacterial secretory proteins are more structurally disordered than their cytoplasmic counterparts.

Conclusions: We present a publicly available prediction method capable of discriminating between this group of proteins and other proteins, thus allowing for the identification of novel non-classically secreted proteins. We suggest candidates for non-classically secreted proteins in Escherichia coli and Bacillus subtilis. The prediction method is available at http://www.cbs.dtu.dk/services/SecretomeP-2.0/.

PMID: 16212653

doi: 10.1186/1471-2180-5-58




CORRESPONDENCE

Henrik Nielsen,