Prediction of twin-arginine signal peptides. BMC Bioinformatics 6:167

Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Building 208, DK-2800, Lyngby, Denmark.
BMC Bioinformatics (Impact Factor: 2.58). 02/2005; 6(1):167. DOI: 10.1186/1471-2105-6-167
Source: PubMed


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.
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.
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

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Available from: Jannick Bendtsen
    • "Note that the sensitivity and false positive rate values depicted here are not cross-validation performances, but measured by applying the finished method to the whole dataset Gram-positive bacteria (Tjalsma et al. 2000). The available servers are TatFind 19 (Rose et al. 2002), which is based on a regular expression combined with a set of simple rules concerning hydrophobicity and charge, TatP 20 (Bendtsen et al. 2005b), which is based on a regular expression combined with two ANNs, and the newer HMM-based PRED-TAT 21 (Bagos et al. 2010). In addition, there are three motifs available in the family and domain databases: the PROSITE profile TAT, 22 the Pfam profile TAT_signal, 23 and the TIGRFAMs profile TAT_signal_seq. "
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    • "SignalP/, model for Gram-negative bacteria; Petersen et al., 2011) and TatP (; Bendtsen et al., 2005) for GSP and twin-arginine protein translocation (TAT) signal peptides, respectively. Proteins were also analyzed using OCTOPUS in cases of manually identified Cytc candidates with no result in the N-terminal TMH prediction. "
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    • "The hydrophobic H domains were determined with the ExPASy ProtScale Tool according to Kyte & Doolittle [69]. Twin arginine motifs (RRxFLk) were predicted with TatP [70]. Transmembrane helices were determined by the TMHMM [71] algorithm. "
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