Predicting Secretory Proteins with SignalP

Part of the Methods in Molecular Biology book series (MIMB, volume 1611)


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.


Signal peptides Prediction Secretion Protein sorting Protein subcellular location 



Heartfelt thanks go to all coauthors on the SignalP papers though the years: Søren Brunak, Jacob Engelbrecht, Gunnar von Heijne, Anders Krogh, Jannick Dyrløv Bendtsen, and Thomas Nordahl Petersen. In addition, I wish to thank the people who helped in implementing the website and still work on keeping it up and running: Kristoffer Rapacki, Hans Henrik Stærfeldt, and Peter Wad Sackett.


  1. 1.
    von Heijne G (1990) The signal peptide. J Membr Biol 115:195–201. doi: 10.1007/BF01868635 CrossRefGoogle Scholar
  2. 2.
    Pohlschröder M, Prinz WA, Hartmann E, Beckwith J (1997) Protein translocation in the three domains of life: variations on a theme. Cell 91:563–566. doi: 10.1016/S0092-8674(00)80443-2 CrossRefPubMedGoogle Scholar
  3. 3.
    Dalbey RE, Lively MO, Bron S, Dijl JMV (1997) The chemistry and enzymology of the type I signal peptidases. Protein Sci 6:1129–1138. doi: 10.1002/pro.5560060601 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    von Heijne G (1988) Transcending the impenetrable: how proteins come to terms with membranes. Biochim Biophys Acta Rev Biomembr 947:307–333. doi: 10.1016/0304-4157(88)90013-5 CrossRefGoogle Scholar
  5. 5.
    Harter C, Wieland F (1996) The secretory pathway: mechanisms of protein sorting and transport. Biochim Biophys Acta Rev Biomembr 1286:75–93. doi: 10.1016/0304-4157(96)00003-2 CrossRefGoogle Scholar
  6. 6.
    Ferguson MAJ, Williams AF (1988) Cell-surface anchoring of proteins via Glycosyl-phosphatidylinositol structures. Annu Rev Biochem 57:285–320. doi: 10.1146/ CrossRefPubMedGoogle Scholar
  7. 7.
    Duong F, Eichler J, Price A et al (1997) Biogenesis of the gram-negative bacterial envelope. Cell 91:567–573. doi: 10.1016/S0092-8674(00)80444-4 CrossRefPubMedGoogle Scholar
  8. 8.
    Mazmanian SK, Liu G, Ton-That H, Schneewind O (1999) Staphylococcus aureus Sortase, an enzyme that anchors surface proteins to the Cell Wall. Science 285:760–763. doi: 10.1126/science.285.5428.760 CrossRefPubMedGoogle Scholar
  9. 9.
    von Heijne G (1983) Patterns of amino acids near signal-sequence cleavage sites. Eur J Biochem 133:17–21. doi: 10.1111/j.1432-1033.1983.tb07424.x CrossRefGoogle Scholar
  10. 10.
    McGeoch DJ (1985) On the predictive recognition of signal peptide sequences. Virus Res 3:271–286. doi: 10.1016/0168-1702(85)90051-6 CrossRefPubMedGoogle Scholar
  11. 11.
    von Heijne G (1986) A new method for predicting signal sequence cleavage sites. Nucleic Acids Res 14:4683–4690. doi: 10.1093/nar/14.11.4683 CrossRefGoogle Scholar
  12. 12.
    Ladunga I, Czakó F, Csabai I, Geszti T (1991) Improving signal peptide prediction accuracy by simulated neural network. Comput Appl Biosci 7:485–487. doi: 10.1093/bioinformatics/7.4.485 PubMedGoogle Scholar
  13. 13.
    Schneider G, Wrede P (1993) Development of artificial neural filters for pattern recognition in protein sequences. J Mol Evol 36:586–595. doi: 10.1007/BF00556363 CrossRefPubMedGoogle Scholar
  14. 14.
    Nielsen H, Brunak S, Engelbrecht J, von Heijne G (1997) Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng 10:1–6. doi: 10.1093/protein/10.1.1 CrossRefPubMedGoogle Scholar
  15. 15.
    Nielsen H, Engelbrecht J, Brunak S, Heijne GV (1997) A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Int J Neural Syst 8:581–599. doi: 10.1142/S0129065797000537 CrossRefPubMedGoogle Scholar
  16. 16.
    Nielsen H, Krogh A (1998) Prediction of signal peptides and signal anchors by a hidden Markov model. Proc Int Conf Intell Syst Mol Biol 6:122–130PubMedGoogle Scholar
  17. 17.
    Bendtsen JD, Nielsen H, von Heijne G, Brunak S (2004) Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 340:783–795. doi: 10.1016/j.jmb.2004.05.028 CrossRefPubMedGoogle Scholar
  18. 18.
    Petersen TN, Brunak S, von Heijne G, Nielsen H (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785–786. doi: 10.1038/nmeth.1701 CrossRefPubMedGoogle Scholar
  19. 19.
    Menne KML, Hermjakob H, Apweiler R (2000) A comparison of signal sequence prediction methods using a test set of signal peptides. Bioinformatics 16:741–742. doi: 10.1093/bioinformatics/16.8.741 CrossRefPubMedGoogle Scholar
  20. 20.
    Klee E, Ellis L (2005) Evaluating eukaryotic secreted protein prediction. BMC Bioinformatics 6:1–7. doi: 10.1186/1471-2105-6-256 CrossRefGoogle Scholar
  21. 21.
    Choo K, Tan T, Ranganathan S (2009) A comprehensive assessment of N-terminal signal peptides prediction methods. BMC Bioinformatics 10:S2. doi: 10.1186/1471-2105-10-S15-S2 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Zhang X, Li Y, Li Y (2009) Evaluating signal peptide prediction methods for gram-positive bacteria. Biologia (Bratisl) 64:655–659. doi: 10.2478/s11756-009-0118-3 Google Scholar
  23. 23.
    Käll L, Krogh A, Sonnhammer EL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338:1027–1036. doi: 10.1016/j.jmb.2004.03.016 CrossRefPubMedGoogle Scholar
  24. 24.
    Reynolds SM, Käll L, Riffle ME et al (2008) Transmembrane topology and signal peptide prediction using dynamic Bayesian networks. PLoS Comput Biol 4:e1000213. doi: 10.1371/journal.pcbi.1000213 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Viklund H, Bernsel A, Skwark M, Elofsson A (2008) SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology. Bioinformatics 24:2928–2929. doi: 10.1093/bioinformatics/btn550 CrossRefPubMedGoogle Scholar
  26. 26.
    Cygwin. Accessed 30 May 2016
  27. 27.
    MobaXterm free Xserver and tabbed SSH client for Windows. Accessed 30 May 2016
  28. 28.
    Fraser CM, Gocayne JD, White O et al (1995) The minimal gene complement of Mycoplasma genitalium. Science 270:397–404. doi: 10.1126/science.270.5235.397 CrossRefPubMedGoogle Scholar
  29. 29.
    Ivankov DN, Payne SH, Galperin MY et al (2013) How many signal peptides are there in bacteria? Environ Microbiol 15:983–990. doi: 10.1111/1462-2920.12105 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    gnuplot homepage. Accessed 30 May 2016
  31. 31.
    Krogh A, Larsson B, von Heijne G, Sonnhammer EL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567–580. doi: 10.1006/jmbi.2000.4315 CrossRefPubMedGoogle Scholar
  32. 32.
    TMHMM Server, v. 2.0. Accessed 30 May 2016
  33. 33.
    Henrik Nielsen D-4128-2011— Accessed 30 May 2016
  34. 34.
    Hon LS, Zhang Y, Kaminker JS, Zhang Z (2009) Computational prediction of the functional effects of amino acid substitutions in signal peptides using a model-based approach. Hum Mutat 30:99–106. doi: 10.1002/humu.20798 CrossRefPubMedGoogle Scholar
  35. 35.
    Ng PC, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812–3814. doi: 10.1093/nar/gkg509 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Vaser R, Adusumalli S, Leng SN et al (2016) SIFT missense predictions for genomes. Nat Protoc 11:1–9. doi: 10.1038/nprot.2015.123 CrossRefPubMedGoogle Scholar
  37. 37.
    SIFT—Predict effects of nonsynonmous/missense variants. Accessed 30 May 2016
  38. 38.
    Adzhubei IA, Schmidt S, Peshkin L et al (2010) A method and server for predicting damaging missense mutations. Nat Methods 7:248–249. doi: 10.1038/nmeth0410-248 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Niroula A, Urolagin S, Vihinen M (2015) PON-P2: prediction method for fast and reliable identification of harmful variants. PLoS One 10:e0117380. doi: 10.1371/journal.pone.0117380 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Hecht M, Bromberg Y, Rost B (2015) Better prediction of functional effects for sequence variants. BMC Genomics 16:S1. doi: 10.1186/1471-2164-16-S8-S1 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Qin W, Li Y, Li J et al (2012) Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributes. Comput Biol Chem 36:31–35. doi: 10.1016/j.compbiolchem.2011.12.001 CrossRefPubMedGoogle Scholar
  42. 42.
    Nielsen H, Engelbrecht J, von Heijne G, Brunak S (1996) Defining a similarity threshold for a functional protein sequence pattern: the signal peptide cleavage site. Proteins Struct Funct Bioinforma 24:165–177. doi: 10.1002/(SICI)1097-0134(199602)24:2<165::AID-PROT4>3.0.CO;2-I CrossRefGoogle Scholar
  43. 43.
    UniProt help: Signal peptide. Accessed 30 May 2016
  44. 44.
    Signal peptide—Wikipedia, the free encyclopedia. Accessed 30 May 2016
  45. 45.
    SO_0000418 < Ontology Lookup Service < EMBL-EBI. Accessed 30 May 2016
  46. 46.
    Emanuelsson O, Nielsen H, Brunak S, von Heijne G (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol 300:1005–1016. doi: 10.1006/jmbi.2000.3903 CrossRefPubMedGoogle Scholar
  47. 47.
    TargetP 1.1 Server. Accessed 30 May 2016
  48. 48.
    von Heijne G (1989) The structure of signal peptides from bacterial lipoproteins. Protein Eng 2:531–534. doi: 10.1093/protein/2.7.531 CrossRefGoogle Scholar
  49. 49.
    Juncker AS, Willenbrock H, von Heijne G et al (2003) Prediction of lipoprotein signal peptides in gram-negative bacteria. Protein Sci 12:1652–1662. doi: 10.1110/ps.0303703 CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    LipoP 1.0 Server. Accessed 30 May 2016
  51. 51.
    Cristóbal S, de Gier J-W, Nielsen H, von Heijne G (1999) Competition between Sec- and TAT-dependent protein translocation in Escherichia coli. EMBO J 18:2982–2990. doi: 10.1093/emboj/18.11.2982 CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Berks BC (2015) The twin-arginine protein translocation pathway. Annu Rev Biochem 84:843–864. doi: 10.1146/annurev-biochem-060614-034251 CrossRefPubMedGoogle Scholar
  53. 53.
    Bendtsen JD, Nielsen H, Widdick D et al (2005) Prediction of twin-arginine signal peptides. BMC Bioinformatics 6:167. doi: 10.1186/1471-2105-6-167 CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    TatP 1.0 Server. Accessed 30 May 2016
  55. 55.
    Pedersen AG, Nielsen H (1997) Neural network prediction of translation initiation sites in eukaryotes: perspectives for EST and genome analysis. Proc Int Conf Intell Syst Mol Biol 5:226–233Google Scholar
  56. 56.
    NetStart 1.0 Prediction Server. Accessed 30 May 2016
  57. 57.
    Thompson BG, Murray RGE (1981) Isolation and characterization of the plasma membrane and the outer membrane of Deinococcus radiodurans strain Sark. Can J Microbiol 27:729–734. doi: 10.1139/m81-111 CrossRefPubMedGoogle Scholar
  58. 58.
    Bird P, Gething MJ, Sambrook J (1987) Translocation in yeast and mammalian cells: not all signal sequences are functionally equivalent. J Cell Biol 105:2905–2914. doi: 10.1083/jcb.105.6.2905 CrossRefPubMedGoogle Scholar
  59. 59.
    Payne SH, Bonissone S, Wu S et al (2012) Unexpected diversity of signal peptides in prokaryotes. mBio 3:e00339–e00312. doi: 10.1128/mBio.00339-12 CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    GFF2—GMOD. Accessed 30 May 2016

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of Bio and Health InformaticsTechnical University of DenmarkLyngbyDenmark

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