Prediction of twin-arginine signal peptides.

Abstract

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 http://www.cbs.dtu.dk/services/TatP/.

Full text

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BioMed Central
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BMC Bioinformatics
Open Access
Methodology article
Prediction of twin-arginine signal peptides
Jannick Dyrløv Bendtsen*1, Henrik Nielsen1, David Widdick2,3,
Tracy Palmer2,3 and Søren Brunak1
Address: 1Center for Biological Sequence Analysis BioCentrum-DTU Building 208 Technical University of Denmark DK-2800 Lyngby, Denmark,
2Department of Molecular Microbiology John Innes Centre Norwich NR4 7UH UK and 3School of Biological Sciences University of East Anglia
Norwich NR4 7TJ UK
Email: Jannick Dyrløv Bendtsen* - jannick@cbs.dtu.dk; Henrik Nielsen - hnielsen@cbs.dtu.dk; David Widdick - david.widdick@bbsrc.ac.uk;
Tracy Palmer - tracy.palmer@bbsrc.ac.uk; Søren Brunak - brunak@cbs.dtu.dk
* Corresponding author
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.
Conclusion: 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/.
Background
The Tat (Twin arginine translocation) pathway, which
operates in parallel to the well characterized Sec export
pathway, has recently been discovered in bacteria [1-3].
Substrates of the Tat pathway are often redox cofactor
binding proteins which acquire their cofactors, and there-
fore fold in the cytoplasm. Thus, in contrast to protein
export via the Sec pathway, Tat substrates are folded prior
to export [1,4]. Indeed, there is good evidence to suggest
that the Tat pathway has the ability to recognize the
folded state of a substrate protein and to reject unfolded
proteins [5,6].
Proteins entering the Tat pathway have signal peptides
with a tripartite structure that is much like classical Sec sig-
nal peptides and indeed are probably also cleaved by
Published: 02 July 2005
BMC Bioinformatics 2005, 6:167doi:10.1186/1471-2105-6-167
Received: 23 March 2005
Accepted: 02 July 2005
This article is available from: http://www.biomedcentral.com/1471-2105/6/167
© 2005 Bendtsen et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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leader peptidase [7]. However, in contrast to Sec signal
peptides, a striking twin-arginine motif is found at the
border between the n- and h-regions of the Tat signal pep-
tide. A consensus sequence for this twin-arginine motif
has previously been defined as (S/T)RRxFLK [1], where
the two consecutive arginines are invariant (see Figure 1).
With an average length of 37 amino acids, Tat signal pep-
tides are significantly longer than classical Sec signal pep-
tides. (The extended length is usually found in the n-
region). In addition, the h-region of Tat signal peptides
has a lower average hydrophobicity than classical Sec sig-
nal peptides [8]. Tat signal peptides are not limited to the
bacterial domain, and they have also been found in
Archaea [9] and in plants where they mediate Tat-depend-
ent translocation across the chloroplast thylakoid mem-
brane [10].
Although the twin-arginine sequence motif has previously
been defined, some variations of this may still be accepted
by the Tat machinery. For example, the tetrathionate
reductase from Salmonella enterica was found to lack one
of the (previously thought) invariant arginines of the
twin-arginine motif [11]. Moreover, analysis of the E. coli
pre-propenicillin amidase Tat signal peptide showed that
it targets the Tat pathway. This signal peptide deviates
from the consensus by having an asparagine between the
two arginines in the twin-arginine motif [12]. On several
occasions, mutational analysis has shown that the two
consecutive arginines are not absolutely required for
export via the Tat translocon. Either arginine of the E. coli
SufI protein could be substituted by lysine without block-
ing Tat-dependent transport, although the rate of trans-
port was reduced [13]. Substituting both arginines for
lysine, or the first arginine for alanine, blocked transport
completely. A similar result was found by Ize et al. [14],
working with the E. coli TorA Tat signal peptide fused to
green fluorescent protein (GFP) [14]. Buchanan et al. [15]
found that the first arginine of the E. coli TorA Tat signal
peptide could be substituted for lysine without blocking
Tat-dependent transport [15]. Also using a TorA-GFP
Tat signal peptides
Figure 1
Tat signal peptides. Sequence logo of the positive training set aligned at the two consecutive arginines. None of the variant
forms of Tat signal peptides were included. Sequence conservation is shown in bits and constructed as previously described
[29].

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construct, DeLisa et al. [16] found that the second arginine
could be substituted not only by lysine but also by aspar-
agine or glutamine without blocking export.
Numerous studies have used regular expressions in com-
bination with SignalP for identification of putative Tat
signal peptides. The SignalP method for prediction of Sec
signal peptides [17,18], can to some extend correctly pre-
dict Tat signal peptides; although the h-regions of Tat sig-
nal peptides tend to be less hydrophobic than classical Sec
signal peptides and SRP signal sequences. However, accu-
racy by the SignalP approach for prediction of Tat signal
peptides is low, and the position of the predicted cleavage
site is often wrongly assigned. This is probably due to the
extended length of the Tat signal peptide and the fact that
frequently the c-regions of Tat signal peptides contain
basic amino acids [8].
Two different targeting pathways converge at the Sec
translocon. Proteins carrying SRP (signal recognition par-
ticle) signal sequences are targeted via SRP to the Sec
machinery, mostly for integration into the membrane.
Proteins carrying Sec signal peptides are translocated
across the bacterial membrane and the signal peptide is
cleaved off in order to release the mature protein. Discrim-
ination of Sec and SRP signals is apparently based on the
hydrophobicity of the signal sequence itself. Increasing
the hydrophobicity of a Sec signal peptide reroutes it to
the SRP targeting pathway [19,20].
Recently, a Perl program for prediction of Tat signal pep-
tides, TATFIND, based on regular expressions was devel-
oped [9,21]. In addition to regular expressions, TATFIND
uses a hydrophobicity measure in a rule based classifica-
tion scheme. Because of a limited number of variant Tat
signal peptides (not containing two consecutive
arginines), the program was designed to ignore these.
Therefore it is not capable of identifying the variant Tat
signal peptides unless the user possesses some program-
ming experience for changing the TATFIND program
code.
In this report we describe a new method, termed TatP, for
the identification of Tat signal peptides. TatP differs from
the previously described TATFIND because it integrates
pattern matching and machine learning. Comparison of
TatP with TATFIND on various independent test sets indi-
cates that TatP has slightly more false negative predictions
than TATFIND. Nevertheless, TatP generates far less false
positive predictions than TATFIND.
Implementation
Data set extraction
All sequence data were extracted from Swiss-Prot release
42.0 [22]. We extracted putative Tat signal peptide
sequences with the database cross-reference identifier
(DR: TAT_signal_seq) from the TIGR database of protein
families (Ac: TIGR01409). The TIGR family of Tat signal
peptides is based on predictions using a hidden Markov
model.
Of the 117 sequences extracted matching the above crite-
ria, twelve were found by comments in the 'CC -!- PTM'
line to have experimental evidence for utilizing the Tat
export pathway and were removed from the positive train-
ing set into an independent test set (see below). Due to
limited amount of data, the positive training set was
homology partitioned, thereby bringing the most homol-
ogous sequences together into 5 different subsets http://
www.cbs.dtu.dk/services/TatP.
1178 cytoplasmic sequences all carrying two consecutive
arginines (RR) within the first 30 amino acids were
extracted from Swiss-Prot. Following redundancy reduc-
tion, a resulting set of 462 cytoplasmic sequences together
with 62 Gram-positive and 70 Gram-negative Sec signal
peptides randomly chosen from the SignalP 3.0 dataset
without the 'Tat_signal_seq' identifier, were used as a neg-
ative training set. The negative training set was homology
reduced using the same scheme as for SignalP [17,23]. In
the negative training set we do not distinguish between
Sec signal peptides or SRP signal sequences.
Twelve Tat substrates (DMSA_ECOLI, MBHS_ALCEU,
MBHS_ALCHY, MBHS_AZOVI,
MBHS_OLICA, MBHS_WOLSU,
OPGD_ECOLI, PHNS_DESVM,
TORA_ECOLI) were removed from the positive training
set into an independent test set, together with two addi-
tional sequences from a mutation study of TorA [8]. This
test set of known Tat substrates is biased, as most of these
proteins function as the small subunit of [Ni-Fe] hydroge-
nases. Therefore, we also included additional 23 Tat sub-
strates from E. coli in the independent test set, having a
total of 35 known Tat substrates. A curated set of E. coli Tat
signal peptides can
www.jic.bbsrc.ac.uk/staff/tracy-palmer/signals.htm.
MBHS_ECOLI,
MBHT_ECOLI,
PHSS_DESBA,
be found at http://
Furthermore, a test set of 15 sequences all with putative
Tat signal peptides from B. subtilis, was found by literature
studies [24,25].
Additionally, we have used the remaining 713 bacterial
cytoplasmic sequences (removed by homology reduction)
all carrying two consecutive arginines (RR) within the first
30 amino acids of each sequence as an evaluation set, as
well as a test set of 632 transmembrane proteins also car-
rying two consecutive arginines within the initial 30
amino acids.

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Regular expressions
Perl syntax regular expressions are allowed as filtering of
potential Tat signal peptides. Furthermore, this assists the
user in identifying the position of a potential Tat signal
peptide motif. A regular expression of 'RR. [FGAVML]
[LITMVF]', where '.' means any amino acid, is used by
default and this regular expression covers 97% of the
sequences found in the training set. This pattern was
extracted from an ungapped multiple sequence alignment
of the positive training set where the position of the two
arginines was fixed.
Neural network architecture
We used two different neural networks for coping with the
Tat signal peptide prediction problem. One network for
recognition of the cleavage site, and one network for
determining whether a given amino acid belongs to the
Tat signal peptide or not (discrimination). A more thor-
ough description of the neural network has been pre-
sented in previous papers [17,23].
Performance evaluation of the prediction method was car-
ried out on different data sets. During training of the
method, we used the same measure of performance as
used in SignalP i.e.cross-validation on the training set.
This means that the data set was split into five equally
sized parts followed by training on four and tested on one
for a total of five times until all sequences independently
had been used for training and testing, respectively.
Furthermore, we evaluated the method on various inde-
pendent test sets as described above.
The cleavage site network was based on an asymmetric
window of 19 positions to the left and 3 positions to the
right of the cleavage site. The cleavage site network had 2
units in the hidden layer. The discrimination network per-
forms best using a symmetric or nearly symmetric win-
dow. The window size used in this work was symmetric
covering 31 positions. The discrimination network had 3
units in the hidden layer. Composition and positioning
information was included in the neural network as previ-
ously described [17].
Description of scores
Five different scores are presented in the output of TatP.
Two scores, the S-score and the C-score, are generated by
the discrimination and cleavage site network and are cal-
culated for each position in the amino acid sequence. The
positioning of the predicted cleavage site is indicated by
the Y-score. The Y-score is defined as,
where ∆dSi is the difference between the average S-score of
d positions before and d positions after position i:
The mean S-score is calculated as the average of the S-score
in the predicted signal peptide region separated by the Y-
score for maximal cleavage site prediction.
As for SignalP version 3.0, the best discrimination of sig-
nal peptides versus non-signal peptides, is calculated as an
average of the mean S-score and the maximal Y-score. We
call it the D-score. If the D-score is above the defined cut-
off value and a Tat signal peptide motif is found the
sequence is nominated as a Tat substrate.
Results and Discussion
Performance evaluation of TatP
The TatP prediction server is a regular expression and neu-
ral network based tool that can be used to predict the pres-
ence of bacterial Tat signal peptides. The user is allowed to
independently specify a preferred consensus pattern by
Perl regular expressions as a filtering of input sequences.
As default, we have implemented a complex consensus
pattern which covers 97% of putative Tat signal peptides
in the training set. The used regular expression is gener-
ated from the training data and can be identified in 103 of
the 105 positive training sequences. The reason for
excluding two sequences was to generate a consensus pat-
tern that was as strict as possible. We allow the user to
change the consensus pattern before running the entire
prediction, which allows for large flexibility of the
method. In eleven of the 105 positive training sequences,
TATFIND did not identify a Tat signal peptide motif. The
strength of allowing the user to choose a preferred consen-
sus pattern is obvious when it comes to prediction of var-
iant Tat signal peptides, which under default conditions
are discarded. Our method uses a regular expression for
identifying putative Tat substrates and a neural network
for coping with the less hydrophobic h-region of the Tat
signal peptide. The h-region is generally less hydrophobic
in Tat signal peptides than classical Sec or SRP targeting
sequences.
A numeric and graphic output is generated to assist the
user to interpret borderline cases and localize the pre-
dicted Tat signal peptide cleavage site (See Figure 2).
For evaluation of TatP, we have tested the performance of
our method in several different ways. We have evaluated
the method on twelve experimentally verified Tat signal
peptides extracted from Swiss-Prot together with a curated
set of E. coli Tat signal peptides. Furthermore, we have
analyzed sequences of TorA (E. coli TMAO reductase)
YCS
ii d i
=
( )
1
∆
,
∆d i i j
−
i j
+
j
d
∑
j
d
S
d
SS
=−
( )
2
=
−
=
∑
1
0
1
1
( ).,

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where the wildtype signal peptide targets a passenger pro-
tein to the Tat pathway, and where mutational analysis
shifted the targeting of the protein to the Sec pathway. We
also examined whether the method is able to discriminate
the Sec signal peptides emerging from the generation of
the training set for SignalP version 3.0 from Tat signal
peptides [17]. A proteomic study on proteins secreted in
Bacillus subtilis was also used for validation of the TatP
method [24,25]. Moreover, we also comment on a few
known cases of the variant Tat signal peptides, as well as
prediction on transmembrane helices.
Discrimination between Sec and Tat signal peptides
Numerous studies have used SignalP for prediction of Tat
signal peptides. To some extent, SignalP is able to cor-
rectly classify Tat signal peptides, although the accuracy is
lower than the TatP method presented here.
Of the 35 known Tat substrates used in this study, only
83% receive a positive prediction by SignalP, even though
they all have the consensus pattern. This shows that the
SignalP can be used for Tat signal peptide prediction,
although a more accurate method is preferable. The
method presented here – TatP – has a higher accuracy
than the approach mentioned above. The TatP method
predicts 91% of the 35 known Tat substrates as having a
Tat signal peptide. This is comparable to TATFIND which
positively predicted 97% of these. Unfortunately, three
known Tat substrates were not correctly identified by TatP,
PHSS_DESBA, YAGT_ECOLI and FHUD_ECOLI. TAT-
FIND was not able to correctly classify YCDO_ECOLI
where it found no consensus motif for a Tat signal
peptide.
In addition to the classification prediction, TatP also
reports a potential cleavage site for the Tat signal peptide
(see Figure 2), a feature which is not reported in the out-
put by TATFIND. Unfortunately, not all of the 35 known
Tat signal peptides have experimentally verified cleavage
sites. Merging information from Swiss-Prot and our
curated E. coli dataset we expect to have a correct cleavage
site annotation in all known Tat substrates. When using
the merged annotations, the TatP method has a correct
cleavage site prediction in 84% of true-positive predicted
Tat substrates.
Assessment of whether TatP and TATFIND were able to
discriminate Tat signal peptides from Sec signal peptides
was carried out on the training set for SignalP version 3.0.
Additionally, we evaluated the performance of TatP and
TATFIND on 713 cytoplasmic proteins carrying two con-
secutive arginines located within the initial 30 amino
Server output
Figure 2
Server output. Graphical output from the prediction server. Here is shown the prediction results from SUFI_ECOLI, a
known Tat substrate. The S-score indicates whether an amino acid belongs to a Tat signal peptide or not. C- and Y-scores indi-
cate the positioning of a potential cleavage site.
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 304050 6070
Score
Position
TatP 1.0 prediction: SUFI_ECOLI
MSLSRRQFIQASGIALCAGAVPLKASAAGQQQPLPVPPLLESRRGQPLFMTVQRAHWSFTPGTRASVW GI
C score
S score
Y score

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acids. By searching these datasets with the regular expres-
sion pattern alone, we find that the false positive rates are
4% and 7% for Gram-positive and Gram-negative pro-
teins, respectively. For cytoplasmic proteins with two con-
secutive arginines within the initial 30 amino acids, the
false positive rate of the regular expression alone is 15%.
TATFIND greatly reduces the amount of false positive pre-
dictions by including a rule-based classification scheme in
addition to regular expressions, resulting in a false posi-
tive rate of 1.4%. In TatP we apply a neural network to
identify the less hydrophobic stretches of the h-region in
Tat signal peptides and the false positive rate is lowered to
only 0.4% (see Table 1). Indeed, this shows the power of
the neural network, which is capable of discriminating
between the hydrophobicity of Sec/SRP and Tat targeting
signals. In the Gram-positive test set, only slightly more
false positives were found by TatP than by TATFIND.
It should be mentioned that some of these false positive
predictions by TatP and TATFIND might represent true Tat
signal peptides in the database. The used Gram-negative
and Gram-positive data sets were extracted from Swiss-
Prot before Tat signal peptides were annotated as such.
Moreover, many of these sequence entries were entered in
the Swiss-Prot database even before the Tat export path-
way was discovered, thus a few Tat signal peptides might
be erroneously annotated.
Positive amino acid residues have a topological effect on
transmembrane helices [26]. It is interesting to see
whether TatP and TATFIND are able to discriminate trans-
membrane helices from Tat signal peptides. 632 proteins
carrying transmembrane domains and also carrying two
consecutive arginines within the initial 30 amino acids
were analyzed. We found 22 proteins to be predicted as
having Tat signal peptides, whereas TATFIND identified
46. Many of these were indeed metalloproteins but had a
transmembrane domain annotation instead of a Tat sig-
nal peptide annotation. The reasons for this may be the
same as described above. Rieske proteins remain
anchored to the membrane by an uncleaved Tat signal
sequence in thylakoids [27], which is also believed to be
the case in prokaryotes since the Fe-S cluster of the protein
must reach the trans-side of the membrane. The exact
amount of true Tat signal peptides in this transmembrane
data set is unknown. Nevertheless, it shows the power of
TatP to identify putative Tat-substrates which are errone-
ously annotated as being transmembrane helices. TAT-
FIND seems to over-predict the transmembrane proteins
having two consecutive arginines, as some of the 46 pre-
dicted proteins are chaperones, which are unlikely to carry
a Tat signal peptide. Prompted by the lack of experimental
evidence for a large number of Tat substrates, we looked
at 22 bacterial genomes, where genes encoding compo-
nents of the Tat translocation system failed to be identi-
fied on the basis of homology [21]. Ideally, no Tat
substrates should be found in these genomes by any Tat
signal peptide prediction method. In these 22 bacterial
genomes, TATFIND identified 20 Tat substrates whereas
TatP identified only 4.
Cristóbal et al. [8] showed that Tat signal peptides are gen-
erally less hydrophobic than both Sec signal peptides and
SRP signal sequences. They demonstrated that the Tat sig-
nal peptide of the E. coli TMAO reductase (TorA) could be
converted into a Sec signal peptide without changing the
RR motif, but instead replacing by the hydrophobic h-
region with a more hydrophobic region taken from a Sec
signal peptide. Both TorA signal peptide mutants and the
wildtype Tat signal peptide carry the consensus pattern.
According to the hydrophobicity discrimination from the
neural network, the wildtype TorA is correctly predicted to
be secreted via the Tat pathway. The TorA [10:10] signal
peptide mutant, which is more hydrophobic than the
wildtype but having the same length, is falsely predicted
positive, while the TorA [19:10] signal peptide mutant,
which is both more hydrophobic and shorter, is correctly
predicted negative. Similar results are found for TATFIND.
Tat signal peptides in B. subtilis
Jongbloed et al. [24] compiled a list of 69 B. subtilis pro-
teins with potential Tat signal peptides. In the light of the
above-mentioned mutation studies which showed that
the first arginine in some cases could be substituted by a
lysine without loss of Tat-dependence, they considered
both RR- and KR-motifs. All sequences with an RRXΦΦ or
Table 1: False positive predictions. The table shows the rate of false positive predictions on Gram-positive, and Gram-negative
proteins which are secreted via the Sec-dependent pathway. Furthermore is shows cytoplasmic proteins and transmembrane helix
proteins, both carrying two consecutive arginines within the initial 30 amino acids. Used methods for prediction are regular expression
(Regex), TATFIND and TatP (regular expressions in combination with a neural network (NN)).
MethodGram+Gram-Cyt. (RR)Transmem (RR)
Regex
TATFIND
TatP (Regex+NN)
4%
1%
3%
7%
6%
5%
15%
1.4%
0.4%
23%
7.0%
3.5%

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KRXΦΦ (where Φ denotes a hydrophobic residue) fol-
lowed by a hydrophobic region were selected from the
genome sequence. Subsequently, the authors looked for
the selected proteins in the B. subtilis secretome. Strik-
ingly, only one of the 69 proteins was actually found to be
Tat-dependent, the phosphodiesterase PhoD. It is posi-
tively predicted by TatP. 13 other proteins were found to
be secreted, but were also secreted by a tat deletion
mutant, indicating Sec-dependence. All of these 13 pro-
teins carry a sequence matching the consensus motif, but
after applying the neural network for analyzing the hydro-
phobicity of these signal peptides all 13 receive a negative
and correct prediction by TatP. Three of the 13 proteins,
LipA, WapA and YolA, were furthermore shown to be
dependent on SecA [24]. TATFIND made one false posi-
tive prediction, but was also capable of correctly predict-
ing twelve Sec-dependent proteins not to carry a Tat signal
peptide. The above mentioned sequences were all pre-
dicted to have a Sec signal peptide using SignalP version
3.0 (without truncation), except for PhoD (See Table 2).
Thus, in the case of B. subtilis, putative Tat signal peptides
were all correctly classified as being Sec signal peptides by
SignalP and none of them received a positive prediction
by TatP, showing the power of regular expression based
searches also incorporating the neural network for hydro-
phobicity discrimination.
Very recently, a second Tat-dependent substrate was iden-
tified in B. subtilis. YwbN was found to be secreted in a
strictly Tat-dependent manner [25]. YwbN is positively
and correctly predicted by TatP.
In the B. subtilis genome, TATFIND predicts seven Tat sig-
nal peptides [21], while TatP with the default regular
expression predicts five. If we follow Jongbloed et al.'s
idea and allow KR- as well as RR-motifs (but keep the rest
of the regular expression unchanged), the number of pos-
itive predictions is ten. The exact number of Tat substrates
in this organism, however, must await experimental
verification.
Variant Tat signal peptides
TatP without the regular expression (or with a variant reg-
ular expression) could potentially predict variant Tat sig-
nal peptides without the canonical twin arginines. The
TtrB subunit of Salmonella enterica tetrathionate reductase
carrying a lysine instead of an arginine in the first position
of the Tat signal peptide motif (Genbank AC:
NP_805056) [11], was correctly and positively predicted
by TatP if the regular expression was expanded with a
lysine in the first position. TATFIND does not directly
allow variations in the used regular expression (consensus
motif), although it can be changed if one has basic pro-
gramming skills. Thus, the TtrB subunit receives a negative
prediction by TATFIND. Moreover, the mutant Tat signal
peptides generated by Stanley et al. [13], Buchanan et al.
[15], Ize et al. [14], and DeLisa et al. [16] are easily
predicted by TatP if the user modifies the regular expres-
sion to allow for the relevant substitutions in the RR posi-
tions. However, the naturally occurring variant Tat signal
peptide reported by Ignatova et al. [12] was unfortunately
not predicted positively by TatP.
We did not specifically investigate Tat signal peptides
fused to heterologous passenger proteins [5,28].
However, we expect the majority of these fusion proteins
to obtain a correct prediction (with the caveat that the pas-
senger protein is competent for export by the Tat path-
Table 2: Putative Tat substrates in B. subtilis. The table shows the predictions by SignalP version 3.0, regular expressions, TATFIND
and TatP on B. subtilis proteins which have a putative Tat signal peptide motif, but nevertheless are secreted via the Sec-dependent
pathway. Only PhoD and YwbN are secreted via the Tat secretion pathway.
ProteinRegexSignalP TATFIND TatP
LipA
AbnA
BglC
BglS
LytD
OppA
PbpX
WapA
WprA
YdhF
YfkN
YhcR
YolA
PhoD
YwbN
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes

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BMC Bioinformatics 2005, 6:167 http://www.biomedcentral.com/1471-2105/6/167
Page 8 of 9
(page number not for citation purposes)
way) as the prediction method mainly uses the N-
terminal region of the protein sequence.
Database annotations
As described above, errors can enter a database due to lack
of knowledge. Many Tat signal peptides are annotated as
Sec signal peptides, simply because the corresponding
sequences entered the database before any knowledge of
the Tat secretion pathway.
From release 42.0 to 44.0 of Swiss-Prot many new Tat sig-
nal peptides have been annotated using the TIGR HMM
(see Methods and Materials). Even though Swiss-Prot is
manually curated, errors have been found regarding anno-
tation of Tat signal peptides. It is doubtful whether the fol-
lowing Swiss-Prot entries carry Tat signal peptides, as the
Tat secretion pathway so far has not been identified in
eukaryotic ER membrane translocation. We have found
the following six eukaryotic sequences carrying the TIGR
Tat signal peptide identifier (TAT_signal_seq), which did
not have associations with chloroplast thylakoid mem-
brane trafficking, CLC7_HUMAN,
CLC7_RAT, CLCC_ARATH,
PSP1_ARATH. The above mentioned cases seem to be
false positive predictions made by the TIGR HMM.
CLC7_MOUSE,
CO1B_HUMAN and
Conclusion
The TatP method presented here integrates regular expres-
sions and a neural network based method for prediction
of twin-arginine (Tat) signal peptides. Our study shows
that regular expressions alone are not capable of correctly
identifying Tat signal peptides. The further addition of a
neural network aids correct classification of Tat signal
peptides and Sec signal peptides. Although the TatP
method only slightly outperforms TATFIND, the method
presented here is far superior to a combination of simple
pattern matching and classical signal peptide prediction
methods. Thus, this method is highly useful as it is pub-
licly available as an easy to use online prediction server. In
the area of investigating variant Tat signal peptides, no
programming skills are needed.
We believe that the TatP prediction method will serve as a
nice complement to the SignalP method for prediction of
both Tat and classical Sec signal peptides.
Availability and requirements
The TatP prediction server is available at http://
www.cbs.dtu.dk/services/TatP/ and the SignalP server for
prediction of classical Sec signal peptides is available at
http://www.cbs.dtu.dk/services/SignalP/. The TatP predic-
tion method is available for the IRIX, SunOS and Linux
platforms and is dependent on a range of standard Unix
tools. A license for both academic and non-academic
users can be obtained by following the instructions found
on the above web page.
Authors' contributions
JDB carried out sequence retrieval, neural network train-
ing and optimization and drafted the manuscript. HN
provided input on the manuscript and method. DW
tested the robustness of the method. TP provided a
curated E. coli dataset and wrote parts of the biological
background. Finally, SB provided general inputs and
improvements to the manuscript.
Acknowledgements
This work was supported by grants from the Danish National Research
Foundation, the Danish Natural Science Research Council, the Danish
Center for Scientific Computing. TP is funded by the BBSRC and MRC. We
also thank Mechthild Pohlschröder for kindly providing the TATFIND pro-
gram and Gunnar von Heijne for valuable comments on the manuscript.
References
1.Berks BC: A common export pathway for proteins binding
complex redox cofactors? Mol Microbiol 1996, 22(3):393-404.
2.Sargent F, Bogsch EG, Stanley NR, Wexler M, Robinson C, Berks BC,
Palmer T: Overlapping functions of components of a bacterial
Sec-independent protein export pathway. Embo J 1998,
17(13):3640-3650.
3.Weiner JH, Bilous PT, Shaw GM, Lubitz SP, Frost L, Thomas GH, Cole
JA, Turner RJ: A novel and ubiquitous system for membrane
targeting and secretion of cofactor-containing proteins. Cell
1998, 93(1):93-101.
4.Berks BC, Palmer T, Sargent F: The Tat protein translocation
pathway and its role in microbial physiology. Adv Microb Physiol
2003, 47:187-254.
5. DeLisa MP, Tullman D, Georgiou G: Folding quality control in the
export of proteins by the bacterial twin-arginine transloca-
tion pathway. Proc Natl Acad Sci U S A 2003, 100(10):6115-6120.
6.Musser SM, Theg SM: Characterization of the early steps of
OE17 precursor transport by the thylakoid DeltapH/Tat
machinery. Eur J Biochem 2000, 267(9):2588-2598.
7.Yahr TL, Wickner WT: Functional reconstitution of bacterial
Tat translocation in vitro. Embo J 2001, 20(10):2472-2479.
8.Cristobal S, de Gier JW, Nielsen H, von Heijne G: Competition
between Sec- and TAT-dependent protein translocation in
Escherichia coli. Embo J 1999, 18(11):2982-2990.
9.Rose RW, Bruser T, Kissinger JC, Pohlschroder M: Adaptation of
protein secretion to extremely high-salt conditions by exten-
sive use of the twin-arginine translocation pathway. Mol
Microbiol 2002, 45(4):943-950.
10. Chaddock AM, Mant A, Karnauchov I, Brink S, Herrmann RG, Klos-
gen RB, Robinson C: A new type of signal peptide: central role
of a twin-arginine motif in transfer signals for the delta pH-
dependent thylakoidal protein translocase. Embo J 1995,
14(12):2715-2722.
11.Hinsley AP, Stanley NR, Palmer T, Berks BC: A naturally occurring
bacterial Tat signal peptide lacking one of the 'invariant'
arginine residues of the consensus targeting motif. FEBS Lett
2001, 497(1):45-49.
12.Ignatova Z, Hornle C, Nurk A, Kasche V: Unusual signal peptide
directs penicillin amidase from Escherichia coli to the Tat
translocation machinery. Biochem Biophys Res Commun 2002,
291(1):146-149.
13. Stanley NR, Palmer T, Berks BC: The twin arginine consensus
motif of Tat signal peptides is involved in Sec-independent
protein targeting in Escherichia coli. J Biol Chem 2000,
275(16):11591-11596.
14.Ize B, Gerard F, Zhang M, Chanal A, Voulhoux R, Palmer T, Filloux A,
Wu LF: In vivo dissection of the Tat translocation pathway in
Escherichia coli. J Mol Biol 2002, 317(3):327-335.
15. Buchanan G, Sargent F, Berks BC, Palmer T: A genetic screen for
suppressors of Escherichia coli Tat signal peptide mutations

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BMC Bioinformatics 2005, 6:167http://www.biomedcentral.com/1471-2105/6/167
Page 9 of 9
(page number not for citation purposes)
establishes a critical role for the second arginine within the
twin-arginine motif. Arch Microbiol 2001, 177(1):107-112.
DeLisa MP, Samuelson P, Palmer T, Georgiou G: Genetic analysis
of the twin arginine translocator secretion pathway in
bacteria. J Biol Chem 2002, 277(33):29825-29831.
Bendtsen JD, Nielsen H, von Heijne G, Brunak S: Improved predic-
tion of signal peptides: SignalP 3.0. J Mol Biol 2004,
340(4):783-795.
Nielsen H, Engelbrecht J, Brunak S, von Heijne G: Identification of
prokaryotic and eukaryotic signal peptides and prediction of
their cleavage sites. Protein Eng 1997, 10(1):1-6.
Bowers CW, Lau F, Silhavy TJ: Secretion of LamB-LacZ by the
signal recognition particle pathway of Escherichia coli. J
Bacteriol 2003, 185(19):5697-5705.
Lee HC, Bernstein HD: The targeting pathway of Escherichia
coli presecretory and integral membrane proteins is speci-
fied by the hydrophobicity of the targeting signal. Proc Natl
Acad Sci U S A 2001, 98(6):3471-3476.
Dilks K, Rose RW, Hartmann E, Pohlschroder M: Prokaryotic uti-
lization of the twin-arginine translocation pathway: a
genomic survey. J Bacteriol 2003, 185(4):1478-1483.
Bairoch A, Apweiler R: The SWISS-PROT protein sequence
database and its supplement TrEMBL in 2000. Nucleic Acids Res
2000, 28(1):45-48.
Nielsen H, Engelbrecht J, Brunak S, von Heijne G: A neural network
method for identification of prokaryotic and eukaryotic sig-
nal peptides and prediction of their cleavage sites. Int J Neural
Syst 1997, 8(5-6):581-599.
Jongbloed JD, Antelmann H, Hecker M, Nijland R, Bron S, Airaksinen
U, Pries F, Quax WJ, van Dijl JM, Braun PG: Selective contribution
of the twin-arginine translocation pathway to protein secre-
tion in Bacillus subtilis. J Biol Chem 2002, 277(46):44068-44078.
Jongbloed JD, Grieger U, Antelmann H, Hecker M, Nijland R, Bron S,
van Dijl JM: Two minimal Tat translocases in Bacillus. Mol
Microbiol 2004, 54(5):1319-1325.
Gafvelin G, Sakaguchi M, Andersson H, von Heijne G: Topological
rules for membrane protein assembly in eukaryotic cells. J
Biol Chem 1997, 272(10):6119-6127.
Molik S, Karnauchov I, Weidlich C, Herrmann RG, Klosgen RB: The
Rieske Fe/S protein of the cytochrome b6/f complex in chlo-
roplasts: missing link in the evolution of protein transport
pathways in chloroplasts?
276(46):42761-42766.
Stanley NR, Sargent F, Buchanan G, Shi J, Stewart V, Palmer T, Berks
BC: Behaviour of topological marker proteins targeted to
the Tat protein transport pathway. Mol Microbiol 2002,
43(4):1005-1021.
Schneider TD, Stephens RM: Sequence logos: a new way to dis-
play consensus sequences. Nucleic Acids Res 1990,
18(20):6097-6100.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.

J Biol Chem 2001,
28.
29.