Prediction of twin-arginine signal peptides.
BACKGROUND
Proteins carrying twin-arginine (Tat) signal peptides are exported into the periplasmic compartment or extracellular environment independently of the classical Sec-dependent translocation pathway. To complement other methods for classical signal peptide prediction we here present a publicly available method, TatP, for prediction of bacterial Tat signal peptides.


RESULTS
We have retrieved sequence data for Tat substrates in order to train a computational method for discrimination of Sec and Tat signal peptides. The TatP method is able to positively classify 91% of 35 known Tat signal peptides and 84% of the annotated cleavage sites of these Tat signal peptides were correctly predicted. This method generates far less false positive predictions on various datasets than using simple pattern matching. Moreover, on the same datasets TatP generates less false positive predictions than a complementary rule based prediction method.


CONCLUSIONS
The method developed here is able to discriminate Tat signal peptides from cytoplasmic proteins carrying a similar motif, as well as from Sec signal peptides, with high accuracy. The method allows filtering of input sequences based on Perl syntax regular expressions, whereas hydrophobicity discrimination of Tat- and Sec-signal peptides is carried out by an artificial neural network. A potential cleavage site of the predicted Tat signal peptide is also reported. The TatP prediction server is available as a public web server at http://www.cbs.dtu.dk/services/TatP/.
Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Building 208, DK-2800, Lyngby, Denmark. jannick@cbs.dtu.dk
Jannick Dyrløv
JD
Bendtsen
Henrik
H
Nielsen
David
D
Widdick
Tracy
T
Palmer
Søren
S
Brunak
D001120:Arginine / Q000378:metabolism*; D001426:Bacterial Proteins / Q000302:isolation & purification* / Q000378:metabolism; D026901:Membrane Transport Proteins / Q000302:isolation & purification* / Q000378:metabolism; D016571:Neural Networks, Computer; D021382:Protein Sorting Signals; D020539:Sequence Analysis, Protein / Q000379:methods
D001426:Bacterial Proteins; D026901:Membrane Transport Proteins; D021382:Protein Sorting Signals; D001120:Arginine