TatP - 1.0

The scores and graphical output is almost identical to the output of the SignalP server. The presented scores are calculated in the same way as for SignalP.

The graphical output from TatP (neural network) comprises three different scores, C, S and Y. Two additional scores are reported in the SignalP3-NN output, namely the S-mean and the D-score, but these are only reported as numerical values.

For each prediction, two different neural networks are used, one for predicting the actual signal peptide and one for predicting the position of the signal peptidase I (SPase I) cleavage site. The S-score for the signal peptide prediction is reported for every single amino acid position in the submitted sequence, with high scores indicating that the corresponding amino acid is part of a signal peptide, and low scores indicating that the amino acid is part of a mature protein.

The C-score is the ``cleavage site'' score. For each position in the submitted sequence, a C-score is reported, which should only be significantly high at the cleavage site. Confusion is often seen with the position numbering of the cleavage site. When a cleavage site position is referred to by a single number, the number indicates the first residue in the mature protein, meaning that a reported cleavage site between amino acid 26-27 corresponds to that the mature protein starts at (and include) position 27.

Y-max is a derivative of the C-score combined with the S-score resulting in a better cleavage site prediction than the raw C-score alone. This is due to the fact that multiple high-peaking C-scores can be found in one sequence, where only one is the true cleavage site. The cleavage site is assigned from the Y-score where the slope of the S-score is steep and a significant C-score is found.

The S-mean is the average of the S-score, ranging from the N-terminal amino acid to the amino acid assigned with the highest Y-max score, thus the S-mean score is calculated for the length of the predicted signal peptide.

The D-score is introduced in SignalP version 3.0 and is a simple average of the S-mean and Y-max score. The score shows superior discrimination performance of secretory and non-secretory proteins to that of the S-mean score which was used in SignalP version 1 and 2. In TatP the D-score is used for final discrimination of secretory vs. non-secretory.

For non-secretory proteins all the scores represented in the TatP-NN output should ideally be very low.

EXAMPLE OF SHORT OUTPUT

>MBHS_ECOLI            length = 70
# Measure  Position  Value  Cutoff  signal peptide?
  max. C    46       0.831   0.48   YES
  max. Y    46       0.826   0.41   YES
  max. S    34       0.923   0.84   YES
  mean S     1-45    0.804   0.46   YES
  max. D     1-45    0.815   0.44   YES
# Most likely cleavage site between pos. 45 and 46: AWA-LE
# Found RRQGV as Tat motif starting at position 12 
Used regex: RR.[FGAVML][LITMVF]
//
>AAT_THEMA             length = 70
# Measure  Position  Value  Cutoff  signal peptide?
  max. C    22       0.279   0.48   NO
  max. Y    22       0.090   0.41   NO
  max. S     6       0.102   0.84   NO
  mean S     1-21    0.057   0.46   NO
  max. D     1-21    0.073   0.44   NO
Used regex: RR.[FGAVML][LITMVF]
//

References

Original method (TatP v. 1.0)

Abstract 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.