Services
SignalP - 6.0
Prediction of Signal Peptides and their cleavage sites in all domains of life
The SignalP 6.0 server predicts the presence of signal peptides and the location of their cleavage
sites in proteins from Archaea, Gram-positive Bacteria, Gram-negative Bacteria and Eukarya.
In Bacteria and Archaea, SignalP 6.0 can discriminate between five types of signal peptides:
Additionally, SignalP 6.0 predicts the regions of signal peptides. Depending on the type, the
positions of n-, h- and c-regions as well as of other distinctive features are predicted.
SignalP 6.0 is based on a
transformer protein language model
with a conditional random field for structured prediction.
Behind the Paper: Check out the blog post about the SignalP 6.0 publication in the Nature Portfolio Bioengineering Community.
History paper: Click here to read "A Brief History of Protein Sorting Prediction", The Protein Journal, 2019
Eukaryotic proteins: Remember, the presence or absence of a signal peptide is not the whole story about the localization of a protein! If you want to find out more about the sorting of your eukaryotic proteins, try the protein subcellular localization predictor DeepLoc. You may also want to check whether proteins with signal peptides have GPI anchors that keep them attached to the outer face of the plasma membrane using the predictor NetGPI.
Submit data
Instructions
1. Specify the input sequences
All the input sequences must be in one-letter amino acid code. The allowed alphabet (identical to UniProt, not case sensitive) is as follows:
All the alphabetic symbols not in the allowed alphabet will be converted to X before processing. All the non-alphabetic symbols, including white space and digits, will be ignored.
The sequences can be input in the following two ways:
-
Paste a single sequence (just the amino acids) or a number of sequences in
FASTA
format into the upper window of the main server page.
- Select a FASTA file on your local disk, either by typing the file name into the lower window or by browsing the disk.
Both ways can be employed at the same time: all the specified sequences will
be processed. However, there may be not more than 5,000 sequences in one submission. The sequences
may not be longer than 10,000 amino acids.
2. Customize your run
- Organism:
You should specify the correct organism of origin either Eukarya or Other. This is done to prevent the prediction of types other than Sec/SPI in eukaryotic proteins. Other includes Archaea, Gram-positive and Gram-negative bacteria. - Output format:
You can choose between two output formats:- Standard
- Appropriate for most users. Shows one plot and one summary per sequence.
- Short
- Convenient if you submit lots of sequences. Shows only one line of output per sequence and no graphics.
- Prediction mode:
You can choose between two prediction modes:- Fast
- Appropriate for most users. Runs a reduced-size version of SignalP 6.0 that accurately predicts probabilities. This model was generated from the slow (full) model using model distillation.
- Slow
- Runs the full SignalP 6.0 model. This is six times slower than the fast version and should be used if accurate region border predictions are needed.
3. Submit the job
Click on the "Submit" button. The status of your job (either 'queued' or 'running') will be displayed and constantly updated until it terminates and the server output appears in the browser window.At any time during the wait you may enter your e-mail address and simply leave the window. Your job will continue; you will be notified by e-mail when it has terminated. The e-mail message will contain the URL under which the results are stored; they will remain on the server for 24 hours for you to collect them.
Example Outputs
By default the server produces the following output for each input sequence. The example below shows the output for thioredoxin domain containing protein 4 precursor (endoplasmic reticulum protein ERp44), taken from the Uniprot entry ERP44_HUMAN. The signal peptide prediction is consistent with the database annotation.One annotation is attributed to each protein, the one that has the highest probability. The protein can have a Sec signal peptide (Sec/SPI), a Lipoprotein signal peptide (Sec/SPII), a Tat signal peptide (Tat/SPI), a Tat lipoprotein signal peptide (Tat/SPII), a Pilin signal peptide (Sec/SPIII) or No signal peptide at all (Other).
If a signal peptide is predicted, the cleavage site position is reported as well.
On the plot, marginal probabilities for signal peptide regions are reported, i.e. Sec/SPI n-region / Tat/SPII h-region. There are also marginal probabilities for residues belonging to regions of the mature protein. To keep plots clean, we exclude regions with very low probabilities. The most likely label sequence, from which the cleavage site is inferred, is also indicated. The positions of the following regions and features are predicted by the model:
- n-region: The n-terminal region of the signal peptide. Reported for Sec/SPI, Sec/SPII, Tat/SPI and Tat/SPII. Labeled as N
- h-region: The center hydrophobic region of the signal peptide. Reported for Sec/SPI, Sec/SPII, Tat/SPI and Tat/SPII. Labeled as H
- c-region: The c-terminal region of the signal peptide, reported for Sec/SPI and Tat/SPI.
- Cysteine: The conserved cysteine in +1 of the cleavage site of Lipoproteins that is used for Lipidation. Labeled as c.
- Twin-arginine motif: The twin-arginine motif at the end of the n-region that is characteristic for Tat signal peptides. Labeled as R.
- Sec/SPIII: These signal peptides have no known region structure.
Example: secretory protein - standard output format
Example: secretory protein - short output format
From the Downloads tab, the user can obtain the results of the run in various formats, i.e. JSON, Prediction summary (results for each submission, 1 line per sequence), Processed entries fasta (a FASTA sequence file containing the sequences of protein that had predicted signal peptides, with the signal peptide removed) and Processed entries gff3 (a file showing the signal peptides feature of those proteins that had predicted signal peptides in GFF3 format).
Training and testing data sets
The datasets for training and testing SignalP 6.0 can be found here. Both datasets are in 3-line FASTA format:
>Uniprot_AC|Kingdom|Type|Partition No amino-acid sequence annotation [S: Sec/SPI signal peptide | T: Tat/SPI or Tat/SPII signal peptide | L: Sec/SPII signal peptide | P: Sec/SPIII signal peptide | I: cytoplasm | M: transmembrane | O: extracellular]
SignalP 6.0 Training set: download
SignalP 5.0 Benchmark set: download
Unpartitioned dataset: download
Additional data
Predictions of SignalP 6.0 in reference proteomes from UniProt release 2021_02, as used in the manuscript.Archaea: download
Eukarya: download
Bacteria: download
Selected reference proteomes from the paper: download
Article abstracts
Main references:- Current version (SignalP v. 6.0)
- Original method (SignalP v. 1.1)
- Update to SignalP v. 2.0
- Update to SignalP v. 3.0
- Update to SignalP v. 4.0
- Update to SignalP v. 4.1
- Update to SignalP v. 5.0
Henrik Nielsen's PhD thesis
Current version (SignalP v. 6.0)
SignalP 6.0 predicts all five types of signal peptides using protein language models.Felix Teufel, José Juan Almagro Armenteros, Alexander Rosenberg Johansen, Magnús Halldór Gíslason, Silas Irby Pihl, Konstantinos D Tsirigos, Ole Winther, Søren Brunak, Gunnar Von Heijne and Henrik Nielsen.
Nature Biotechnology (2021), doi:10.1038/s41587-021-01156-3
Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data.
PMID: 34980915
Original method (SignalP v. 1.1)
Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.Henrik Nielsen, Jacob Engelbrecht, Søren Brunak and Gunnar von Heijne.
Protein Engineering, 10:1-6 (1997).
We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence. The method performs significantly better than previous prediction schemes and can easily be applied on genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server.
PMID: 9051728 (free full text pdf version)
Update to SignalP v. 2.0
Prediction of signal peptides and signal anchors by a hidden Markov model.Henrik Nielsen and Anders Krogh.
Proc Int Conf Intell Syst Mol Biol. (ISMB 6), 6:122-130 (1998).
A hidden Markov model of signal peptides has been developed. It contains submodels for the N-terminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our data, the length distributions for the three regions are significantly different from expectations. For instance, the assigned hydrophobic region is between 8 and 12 residues long in almost all eukaryotic signal peptides. This analysis also makes obvious the difference between eukaryotes, Gram-positive bacteria, and Gram-negative bacteria. The model can be used to predict the location of the cleavage site, which it finds correctly in nearly 70% of signal peptides in a cross-validated test — almost the same accuracy as the best previous method. One of the problems for existing prediction methods is the poor discrimination between signal peptides and uncleaved signal anchors, but this is substantially improved by the hidden Markov model when expanding it with a very simple signal anchor model.
PMID: 9783217
Update to SignalP v. 3.0
Improved prediction of signal peptides: SignalP 3.0.Jannick Dyrløv Bendtsen, Henrik Nielsen, Gunnar von Heijne and Søren Brunak.
J. Mol. Biol., 340:783-795 (2004).
We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, and both components have been updated. Motivated by the idea that the cleavage site position and the amino acid composition of the signal peptide are correlated, new features have been included as input to the neural network. This addition, together with a thorough error-correction of a new data set, have improved the performance of the predictor significantly over SignalP version 2. In version 3, correctness of the cleavage site predictions have increased notably for all three organism groups, eukaryotes, Gram negative and Gram positive bacteria. The accuracy of cleavage site prediction has increased in the range from 6–17 % over the previous version, whereas the signal peptide discrimination improvement mainly is due to the elimination of false positive predictions, as well as the introduction of a new discrimination score for the neural network. The new method has also been benchmarked against other available methods.
PMID: 15223320 doi: 10.1016/j.jmb.2004.05.028
Update to SignalP v. 4.0
SignalP 4.0: discriminating signal peptides from transmembrane regions.Thomas Nordahl Petersen, Søren Brunak, Gunnar von Heijne and Henrik Nielsen.
Nature Methods, 8:785-786 (2011).
This is a Correspondence, it has no abstract.
doi: 10.1038/nmeth.1701
Access to the supplementary materials:
nmeth.1701-S1.pdf
Update to SignalP v. 4.1
Predicting Secretory Proteins with SignalPHenrik Nielsen.
In Kihara, D (ed): Protein Function Prediction (Methods in Molecular Biology vol. 1611) pp. 59-73, Springer 2017.
doi: 10.1007/978-1-4939-7015-5_6
PMID: 28451972
SignalP is the currently most widely used program for prediction of signal peptides from amino acid sequences. Proteins with signal peptides are targeted to the secretory pathway, but are not necessarily secreted. After a brief introduction to the biology of signal peptides and the history of signal peptide prediction, this chapter will describe all the options of the current version of SignalP and the details of the output from the program. The chapter includes a case study where the scores of SignalP were used in a novel way to predict the functional effects of amino acid substitutions in signal peptides.
Update to SignalP v. 5.0
SignalP 5.0 improves signal peptide predictions using deep neural networks.José Juan Almagro Armenteros, Konstantinos D. Tsirigos, Casper Kaae Sønderby, Thomas Nordahl Petersen, Ole Winther, Søren Brunak, Gunnar von Heijne and Henrik Nielsen.
Nature Biotechnology, 37, 420-423, doi:10.1038/s41587-019-0036-z (2019)
Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.
PMID: 30778233
Other publications
Locating proteins in the cell using TargetP,
SignalP, and related tools
Olof Emanuelsson, Søren Brunak, Gunnar von Heijne, Henrik Nielsen
Nature Protocols, 2:953-971 (2007).
Determining the subcellular localization of a protein is an important first step toward understanding its function. Here, we describe the properties of three well-known N-terminal sequence motifs directing proteins to the secretory pathway, mitochondria and chloroplasts, and sketch a brief history of methods to predict subcellular localization based on these sorting signals and other sequence properties. We then outline how to use a number of internet-accessible tools to arrive at a reliable subcellular localization prediction for eukaryotic and prokaryotic proteins. In particular, we provide detailed step-by-step instructions for the coupled use of the amino-acid sequence-based predictors TargetP, SignalP, ChloroP and TMHMM, which are all hosted at the Center for Biological Sequence Analysis, Technical University of Denmark. In addition, we describe and provide web references to other useful subcellular localization predictors. Finally, we discuss predictive performance measures in general and the performance of TargetP and SignalP in particular.
PMID: 17446895
Please click
here to access the
paper and supplementary materials.
Machine learning approaches to the prediction of signal peptides
and other protein sorting signals.
Henrik Nielsen, Søren Brunak, and Gunnar von Heijne.
Protein Engineering, 12:3-9 (1999), Review.
Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently, the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, have made it possible to achieve a level of reliability where practical use in, for example automatic database annotation is feasible. In this review, we concentrate on the present status and future perspectives of SignalP, our neural network-based method for prediction of the most well-known sorting signal: the secretory signal peptide. We discuss the problems associated with the use of SignalP on genomic sequences, showing that signal peptide prediction will improve further if integrated with predictions of start codons and transmembrane helices. As a step towards this goal, a hidden Markov model version of SignalP has been developed, making it possible to discriminate between cleaved signal peptides and uncleaved signal anchors. Furthermore, we show how SignalP can be used to characterize putative signal peptides from an archaeon, Methanococcus jannaschii. Finally, we briefly review a few methods for predicting other protein sorting signals and discuss the future of protein sorting prediction in general.
PMID: 10065704
A neural network method for identification of prokaryotic and eukaryotic
signal peptides and prediction of their cleavage sites.
Henrik Nielsen, Jacob Engelbrecht, Søren Brunak
and Gunnar von Heijne.
Int. J. Neural Sys., 8:581-599 (1997).
We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs significantly better than previous prediction schemes, and can easily be applied to genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server: http://www.cbs.dtu.dk/services/SignalP/.
PMID: 10065837
Defining a similarity threshold for a functional protein sequence pattern:
the signal peptide cleavage site.
Henrik Nielsen, Jacob Engelbrecht, Gunnar von Heijne
and Søren Brunak.
Proteins, 24:165-77 (1996).
When preparing data sets of amino acid or nucleotide sequences it is necessary to exclude redundant or homologous sequences in order to avoid overestimating the predictive performance of an algorithm. For some time methods for doing this have been available in the area of protein structure prediction. We have developed a similar procedure based on pair-wise alignments for sequences with functional sites. We show how a correlation coefficient between sequence similarity and functional homology can be used to compare the efficiency of different similarity measures and choose a nonarbitrary threshold value for excluding redundant sequences. The impact of the choice of scoring matrix used in the alignments is examined. We demonstrate that the parameter determining the quality of the correlation is the relative entropy of the matrix, rather than the assumed (PAM or identity) substitution mode. Results are presented for the case of prediction of cleavage sites in signal peptides. By inspection of the false positives, several errors in the database were found. The procedure presented may be used as a general outline for finding a problem-specific similarity measure and threshold value for analysis of other functional amino acid or nucleotide sequence patterns.
PMID: 8820484
From sequence to sorting: Prediction of signal peptides.
Henrik Nielsen.
Ph.D. thesis, defended at Department of Biochemistry,
Stockholm University, Sweden, May 25, 1999.
In the present age of genome sequencing, a vast number of predicted
genes are initially known only by their putative nucleotide
sequence. The newly established field of bioinformatics is concerned
with the computational prediction of structural and functional
properties of genes and the proteins they encode, based on their
nucleotide and amino acid sequences.
Since one of the crucial properties of a protein is its subcellular
location, prediction of protein sorting is an important question in
bioinformatics. A fundamental distinction in protein sorting is that
between secretory and non-secretory proteins, determined by a
cleavable N-terminal sorting signal, the secretory signal peptide.
The main part of this thesis, including four of the six papers,
concerns prediction of secretory signal peptides in both eukaryotic
and bacterial data using two machine learning techniques: artificial
neural networks and hidden Markov models. A central result is the
SignalP prediction method, which has been made available as a World
Wide Web server and is very widely used.
Two additional prediction methods are also included, with one paper
each. ChloroP predicts chloroplast transit peptides, another
cleavable N-terminal sorting signal; while NetStart predicts start
codons in eukaryotic genes. For prediction of all N-terminal signals,
the assignment of correct start codon can be critical, which is why
prediction of translation initiation from the nucleotide sequence is
also important for protein sorting prediction.
This thesis comprises a detailed review of the molecular biology of
protein secretion, a short introduction to the most important machine
learning algorithms in bioinformatics, and a critical review of
existing methods for protein sorting prediction. In addition, it
contains general treatment of the principles of data set construction
and performance evaluation for prediction methods in bioinformatics.
Access to the thesis (without the six included papers): PhDthesis.pdf; PhDthesis-cover.pdf
Frequently Asked Questions
Changes from version 5 to 6Changes from version 4 to 5
Changes from version 4.0 to 4.1
Changes from version 3 to 4
Biological background, signal peptides
Biological background, other sorting signals
Biological background, organism groups
History
Changes from version 5 to 6
— What's new?
Please see the version history page.— What happened to the organism group selection?
SignalP 6.0 is based on a protein language model, which makes it capable of understanding the phylogenomic context of a protein from its amino acid sequence directly. The model does no longer require the organism information for prediction.— What are the fast and slow model modes?
The protein language model on which SignalP 6.0 is built is computationally very expensive. To enable a prediction speed comparable to previous versions, we created a model of reduced size that emulates the output of the larger (slow) model. We recommend the fast model for most applications, i.e. predicting SPs in a large number of unknown sequences. For detailed analysis of SP regions the slow model should be used. The creation of the fast model is described in the supplementary material of the manuscript.Changes from version 4 to 5
— What's new?
Please see the version history page.— What happened to the C-, S- and Y-scores?
The output layer of SignalP 5.0 is a conditional random field (CRF) which yields marginal probabilities, just like the HMM module did in SignalP versions 2 and 3. Since the CRF is a grammatical method which is aware that there can only be one cleavage site in a given signal peptide, there is no need for the post-processing of the network output that was represented by the Y-score.Changes from version 4.0 to 4.1
— What's new?
Please see the version history page.— Why do you present a choice between two cutoff settings? Can't you just decide on one?
The optimal cutoff really depends on what you want to use the method for. If it is important to find all signal peptides, use the sensitive cutoff. If you want an estimate of the number of signal peptides in a genome, use the default cutoff.— Why have you imposed a minimum length?
Because we believe that predictions of signal peptides shorter than ten residues made by SignalP 4.1 are false. The shortest known signal peptides are 11 residues long (with one exception, SP23_TENMO, which does not look like a signal peptide at all). Click here for an updated list of experimentally confirmed signal peptides from UniProt of length 11 or shorter.— What happened to the Background page?
It's here! The important material from the Background page has been integrated into this FAQ, we hope you like the new format.Changes from version 3 to 4
— What's new?
Please see the version history page.— What happened to the HMM part?
While making SignalP 4.0, we did retrain the Hidden Markov Model (HMM) part of SignalP. However, we found that it did not perform better than the neural networks in any of the performance parameters we tested. Therefore, we decided not to include it. If the HMM output is important for you, you can still use SignalP 3.0.— Why is my favourite signal peptide no longer predicted correctly? SignalP 3.0 could do it!
As explained on the performance page, SignalP 4 with the default cutoff has a lower sensitivity than SignalP 3. Please try again with the new "Sensitive" setting.— What happened to the Yes/No answers for max C score etc.?
SignalP 3.0 provided five Yes/No answers for the NN part. We found that this was confusing for users and obscured the fact that the D-score is the best score for discriminating between signal peptides and non-signal peptides.Biological background, signal peptides
— What are signal peptides?
The term "signal peptide" is used with two meanings: In the broad sense (used in many textbooks), a signal peptide is any sorting signal embedded in the amino acid sequence of a protein. In the narrow sense (used in most of the scientific literature), a signal peptide is an N-terminal signal that directs the protein across the ER membrane in eukaryotes and across the plasma membrane in prokaryotes. Signal peptides in the narrow sense are also known as ER signal peptides or secretory signal peptides. Read more in UniProt, in Wikipedia, and in the Sequence feature ontology.
It is important to emphasize that SignalP predicts signal peptides in the narrow sense only.
— Are signal peptides always N-terminal?
In the narrow sense: Yes, per definition. In the broad sense: No, there are several sorting signal that are C-terminal (e.g. the PTS1 signal for peroxisomal import) or internal (e.g. the nuclear localization signal).
— Are signal peptides (in the narrow sense) always cleaved?
No, there are rare cases of uncleaved signal peptides. For an updated list of such proteins annotated in UniProt, click here. These should not be confused with signal anchors, see below.— Which protease is responsible for signal peptide (Sec/SPI) cleavage?
In bacteria, it is Signal Peptidase I (SPase I), also known as Leader Peptidase (Lep). In eukaryotes, it is the signal peptidase complex (SPC), which consists of four subunits in yeast and five in mammals. Read more in MEROPS.— My protein has a signal peptide. Can I then safely conclude that it is secreted?
No. You can only conclude that it enters the secretory pathway.In eukaryotes, there are several opportunities for a protein with a signal peptide to escape secretion. It could:
- be retained in the endoplasmic reticulum (ER). Soluble ER-resident proteins have a C-terminal retention signal with the consensus sequence KDEL, see PROSITE.
- be retained in the Golgi apparatus,
- be directed to the lysosome (vacuole in plants and fungi),
- have one or more transmembrane helices and therefore be retained in either the plasma membrane, or one of the membranes of the secretory pathway (ER, Golgi, lysosome/vacuole), or
- have a signal for GPI-anchoring, a C-terminal cleaved peptide which functions as a signal for attachment of a Glycophosphatidylinositol (GPI) group that anchors the protein to the outer face of the plasma membrane.
- have one or more transmembrane helices, or
- be attached to the cell wall.
In Gram-negative bacteria, a protein with a signal peptide could:
- have one or more transmembrane helices,
- be retained in the periplasm, or
- be inserted into the outer membrane as a β-barrel transmembrane protein.
— Does SignalP predict signal peptides of bacterial and archaeal lipoproteins?
Yes. Bacterial lipoproteins have special signal peptides (Sec/SPII) which are cleaved by Signal Peptidase II (SPase II), also known as Lipoprotein signal peptidase (Lsp). A diacylglyceryl group is attached to a Cysteine residue in position +1 relative to the cleavage site, which bears no resemblance to the SPase I cleavage site. See also MEROPS and PROSITE.— Does SignalP predict Tat (Twin-arginine translocation) signal peptides?
Yes. Bacterial and archaeal Tat signal peptides (Tat/SPI), which direct their proteins through an alternative translocon (TatABC instead of SecYEG), have a special motif, usually containing two Arginines, in the n-region. Additionally, they are in general longer and less hydrophobic than "normal" (Sec) signal peptides. See also PROSITE and InterPro.Biological background, other sorting signals
— What are signal anchors?
A signal anchor is a transmembrane helix located close to the N-terminus of a protein with an N-in orientation (i.e. the N-terminus is on the cytoplasmic side of the membrane). It functions much like a signal peptide since it is recognized by the Signal Recognition Particle (SRP) and inserted into the translocon; but instead of being cleaved and degraded it remains in the membrane and anchors the protein to it. Proteins anchored in this way are known as Type II transmembrane proteins.
 |
Signal peptides (above) versus signal anchors (below) |
The introduction of the Hidden Markov Model (HMM) method in SignalP version 2 made it possible to some extent to distinguish signal peptides from signal anchors (in that version, only in eukaryotes). However, SignalP 4 (based entirely on the Neural Network (NN) method), does a better job, since its negative set is not confined only to transmembrane helices annotated as signal anchors, but includes all types of transmembrane segments close to the N-terminus.
— What should I use for predicting signal peptides in the broad sense?
For mitochondrial and plastid import signals, also known as transit peptides, we recommend TargetP. For general prediction of subcellular location in eukaryotes, we recommend DeepLoc.— What should I use for predicting non-classical (leaderless) secreted proteins?
Not all secretory proteins carry signal peptides. Some proteins enter a non-classical secretory pathway without any currently known sequence motif. In eukaryotes, these proteins are mostly growth factors and extracellular matrix binding proteins. In Gram-negative bacteria, the type I, III, IV and VI secretion systems function without signal peptides. For prediction of such proteins we recommend the SecretomeP server.Biological background, organism groups
— Which version should I use for vira and bacteriophages?
You should use the version corresponding to the host organism. There are some indications that viral signal peptides differ from those of the host organism, but SignalP currently does not take that into account.— Which version should I use for Tenericutes/Mollicutes (Mycoplasma and related genera)?
You shouldn't use SignalP at all for these organisms, since they seem to lack a type I signal peptidase completely!— Which version should I use for metagenomic sequences of unknown origin?
This is an unsolved question. Please use all four versions to search for signal peptides in such data.— Is one version enough for all eukaryotic organisms, or are there differences within the eukaryotes?
It is known that some yeast signal peptides are not recognized by mammalian cells (Bird et al., 1987 and 1990). Therefore, it would be natural to assume that separate SignalP versions for yeast and Mammalia would provide better predictions than a common eukaryotic version. While developing SignalP 4.0 we tried dividing the eukaryotic data into animals, fungi, and plants and training separate methods for these three groups. However, this did not give any improvement, and performance for all three groups was better when using the method trained on all eukaryotic sequences together.— Are two versions enough for all bacteria, or are there differences within the Gram-positive/Gram-negative bacterial groups?
The Gram-negative version of SignalP is almost certainly biased towards E. coli and other γ-proteobacteria, since these constitute the bulk of the experimentally annotated bacterial proteins in UniProt. Unpublished results suggest that some bacteria have very divergent cleavage site motifs. Future versions of SignalP might therefore divide the Gram-negative bacteria into several classes, if data are available.Gram-positive bacteria probably constitute a more homogenous group, but it is an open question whether there are differences in signal peptides between Actinobacteria (high G+C Gram-positive bacteria) and Firmicutes (low G+C Gram-positive bacteria). More data on Actinobacteria are needed before that can be answered.
History
— How are the various versions of SignalP related?
Please see the version history page— Was there ever a Nobel prize awarded for signal peptides?
Yes, for signal peptides in the broad sense. The importance of signal peptides was emphasized in 1999 when Günter Blobel received the Nobel Prize in physiology or medicine for his discovery "proteins have intrinsic signal that govern their transport and localization in the cell". See the press release.— Was SignalP the first signal peptide predictor?
No, but it was, to our knowledge, the first to be implemented as a web server (in 1996). Among the earlier methods were McGeoch (1985) and von Heijne (1986), both of which have been included in PSORT.— How many times have the SignalP papers been cited?
This information is available on Henrik Nielsen's ResearcherID, Scopus, and Google Scholar pages.Version history
Please click on the version number to activate the corresponding server where available.
| 6.0 |
The current server. New in this version:
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| 5.0 |
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| 4.1 |
New in this version:
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| 4.0 |
New in this version:
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| 3.0 |
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| 2.0 |
New in this version:
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| 1.1 |
The original server: the method based on artificial
neural networks.
Main publication:
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Portable version
Would you prefer to run SignalP at your own site? SignalP 6.0 is available as a Python package, with the same functionality as this service. There is a download page for academic users; other users are requested to contact DTU Health Technology Software Package Manager at