Non-classical protein secretion in bacteria

By Jannick Dyrløv Bendtsen, Lars Kiemer, Anders Fausbøll, Søren Brunak. BMC Microbiology, 2005

694 citations recorded by the source corpus.

BackgroundWe 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.ResultsA 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. 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. 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.ConclusionWe 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 online.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/45fb1ce1e67e42e17c0291bf5af862c161a3c986

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In Genetics

  1. Henrik Nielsen
  2. G. von Heijne
  3. Søren Brunak

In Molecular Biology

  1. Henrik Nielsen
  2. Søren Brunak
  3. G. von Heijne
  4. Kuo-Chen Chou
  5. Olof Emanuelsson
  6. Hongbin Shen
  7. Burkhard Rost
  8. David T. Jones

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