Article abstractsMain references:
Ver 1.0 (mammalian sequences, original publication)
Feature based prediction of non-classical and leaderless protein secretion.
J. Dyrløv Bendtsen1, L. Juhl Jensen1, N. Blom1, G. von Heijne2 and S. Brunak1.
Protein Eng. Des. Sel., 17(4):349-356, 2004
Center for Biological Sequence Analysis, The Technical University of Denmark,
DK-2800 Lyngby, Denmark
We present a sequence based method - SecretomeP - for prediction of mammalian secretory proteins targeted to the non-classical secretory pathway, i.e. proteins without an N-terminal signal peptide. So far only a limited number of proteins have been shown experimentally to enter the non-classical secretory pathway. These are mainly fibroblast growth factors, interleukins and galectins found in the extracellular matrix. We have discovered that certain pathway independent features are shared among secreted proteins. The method presented here is also capable of predicting (signal peptide containing) secretory proteins where only the mature part of the protein has been annotated, or cases where the signal peptide remains uncleaved. By scanning the entire human proteome we identify new proteins potentially undergoing non-classical secretion.
Ver 2.0 (mammalian and bacterial sequences)Note: the article below describes only the new (bacterial) part of the method.
Non-classical protein secretion in bacteria
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/.