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
Stockholm Bioinformatics Center, Department of Biochemistry,
Stockholm University, S-106 91 Stockholm, Sweden
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
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
J. D. Bendtsen, L. Kiemer, A. Fausbøll and S. Brunak.
BMC Microbiology, 5:58, 2005
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
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
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