Please note that this is an old version of SignalP. The current version can be found at: http://www.cbs.dtu.dk/services/SignalP/

SignalP V2.0.b2

The SignalP server predicts the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms: Gram-positive prokaryotes, Gram-negative prokaryotes, and eukaryotes. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks and hidden Markov models.

This is a beta version of the new SignalP, SignalP V2.0 . The documentation is not fully updated, and the interface may not be completely stable yet. Please send comments and bug reports to Kristoffer Rapacki.

Instructions

Output format

Important

New in SignalP V2.0:

Incorporation of a hidden Markov model version:

SignalP V2.0 comprises two signal peptide prediction methods, SignalP-NN (based on neural networks, corresponding to SignalP V1.1) and SignalP-HMM (based on hidden Markov models). For eukaryotic data, SignalP-HMM has a substantially improved discrimination between signal peptides and uncleaved signal anchors, but it has a slightly lower accuracy in predicting the precise location of the cleavage site. The user can choose whether to run SignalP-NN, SignalP-HMM, or both.

Retraining of the neural networks

SignalP-NN in SignalP V2.0 is trained on a newer data set derived from SWISS-PROT rel. 35 (instead of rel. 29 as in SignalP V1.1).

Graphics integrated in the output:

SignalP V2.0 shows signal peptide and cleavage site scores for each position as plots in GIF format on the output page. The plots provide more information than the prediction summary, e.g. about possible cleavage sites other than the strongest prediction.

Signal peptide region assignment:

SignalP-HMM provides not only a prediction of the presence of a signal peptide and the position of the cleavage site, but also an approximate assignment of n-, h- and c-regions within the signal peptide. These are shown in the graphical output as probabilities for each position being in one of these three regions.

Automatic truncation

In SignalP V1.1, we recommended that you should submit only the N-terminal part of each protein, not more than 50-70 amino acids. SignalP V2.0 now offers to truncate your sequences automatically.

Click here to go directly to submission form

The method is described in

• 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).

  Protein Engineering has made the full-text PDF version of this paper freely available online at the Red Hot Research Page.

• Prediction of signal peptides and signal anchors by a hidden Markov model. Henrik Nielsen and Anders Krogh, In Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology (ISMB 6 ), AAAI Press, Menlo Park, California, pp. 122--130 (1998).

• Review. 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).

  Abstract

• 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).

  Abstract

• From sequence to sorting: Prediction of signal peptides. Henrik Nielsen, Ph.D. thesis. Defended at Department of Biochemistry, Stockholm University, Sweden, May 25 1999.

  Abstract

• Hot Paper in BIOINFORMATICS - an interview by Eugene Russo with SignalP author Henrik Nielsen.
  The Scientist Volume 13, #13, p. 8, June 21, 1999.

Mail server

Portable version

Would you prefer to run SignalP at your own site? SignalP 2.0 is available as a package on a commercial license. Currently available platforms include MIPS (under IRIX, Silicon Graphics), SPARC (under Solaris, Sun), Alpha (under OSF1) and x86 (under Linux). Send inquiries by e-mail to software@cbs.dtu.dk .