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Amino Acids

, Volume 32, Issue 4, pp 493–496 | Cite as

Predicting secretory protein signal sequence cleavage sites by fusing the marks of global alignments

  • D.-Q. Liu
  • H. Liu
  • H.-B. Shen
  • J. Yang
  • K.-C. Chou
Article

Summary.

A newly synthesized secretory protein in cells bears a special sequence, called signal peptide or sequence, which plays the role of “address tag” in guiding the protein to wherever it is needed. Such a unique function of signal sequences has stimulated novel strategies for drug design or reprogramming cells for gene therapy. To realize these new ideas and plans, however, it is important to develop an automated method for fast and accurately identifying the signal sequences or their cleavage sites. In this paper, a new method is developed for predicting the signal sequence of a query secretory protein by fusing the results from a series of global alignments through a voting system. The very high success rates thus obtained suggest that the novel approach is very promising, and that the new method may become a useful vehicle in identifying signal sequence, or at least serve as a complementary tool to the existing algorithms of this field.

Keywords: Signal peptide – Cleavage site – Global alignment – Needleman–Wunsch algorithm – Secretory protein 

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References

  1. Arrigo, P, Giuliano, F, Scalia, F, Rapallo, A, Damiani, G 1991Identification of a new motif on nucleic acid sequence data using Kohonen’s self-organizing mapComput Appl Biosci7353357PubMedGoogle Scholar
  2. Baldi, P, Brunak, S 1998Bioinformatics: the machine learning approachMIT PressCambridge/MassGoogle Scholar
  3. Bendtsen, JD, Nielsen, H, von Heijne, G, Brunak, S 2004Improved prediction of signal peptides: SignalP 3.0J Mol Biol340783795PubMedCrossRefGoogle Scholar
  4. Blake, JD, Cohen, FE 2001Pairwise sequence alignment below the twilight zoneJ Mol Biol307721735PubMedCrossRefGoogle Scholar
  5. Chen, C, Zhou, X, Tian, Y, Zou, X, Cai, P 2006Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion networkAnal Biochem357116121PubMedCrossRefGoogle Scholar
  6. Chou, KC 2001aPrediction of protein signal sequences and their cleavage sitesProteins Struct Function Genet42136139CrossRefGoogle Scholar
  7. Chou, KC 2001bPrediction of signal peptides using scaled windowPeptides2219731979CrossRefGoogle Scholar
  8. Chou, KC 2001cUsing subsite coupling to predict signal peptidesProtein Eng147579CrossRefGoogle Scholar
  9. Chou, KC 2002Review: Prediction of protein signal sequencesCurr Protein Pep Sci3615622CrossRefGoogle Scholar
  10. Chou, KC 2004Review: Structural bioinformatics and its impact to biomedical scienceCurr Med Chem1121052134PubMedGoogle Scholar
  11. Chou, KC, Shen, HB 2006Predicting protein subcellular location by fusing multiple classifiersJ Cell Biochem99517527PubMedCrossRefGoogle Scholar
  12. Chou, KC, Zhang, CT 1995Review: Prediction of protein structural classesCrit Rev Biochem Mol Biol30275349PubMedGoogle Scholar
  13. Durbin, R, Dear, S 1998Base qualities help sequencing softwareGenome Res8161162PubMedGoogle Scholar
  14. Durbin, RM, Eddy, SR, Krogh, A, Mitchison, G 1998Biological sequence analysisCambridge University PressCambridgeGoogle Scholar
  15. Emanuelsson, O, Nielsen, H, von Heijne, G 1999ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sitesProtein Sci8978984PubMedGoogle Scholar
  16. Feng, ZP 2001Prediction of the subcellular location of prokaryotic proteins based on a new representation of the amino acid compositionBiopolymers58491499PubMedCrossRefGoogle Scholar
  17. Feng, ZP 2002An overview on predicting the subcellular location of a proteinIn Silico Biol2291303PubMedGoogle Scholar
  18. Folz, RJ, Gordon, JI 1987Computer-assisted predictions of signal peptidase processing sitesBiochem Biophys Res Commun146870877PubMedCrossRefGoogle Scholar
  19. Gao, QB, Wang, ZZ, Yan, C, Du, YH 2005Prediction of protein subcellular location using a combined feature of sequenceFEBS Lett57934443448PubMedCrossRefGoogle Scholar
  20. Guo, YZ, Li, M, Lu, M, Wen, Z, Wang, K, Li, G, Wu, J 2006Classifying G protein-coupled receptors and nuclear receptors based on protein power spectrum from fast Fourier transformAmino Acids30397402PubMedCrossRefGoogle Scholar
  21. Ladunga, I, Czako, F, Csabai, I, Geszti, T 1991Improving signal peptide prediction accuracy by simulated neural networkComput Appl Biosci7485487PubMedGoogle Scholar
  22. Liu, H, Yang, J, Ling, JG, Chou, KC 2005Prediction of protein signal sequences and their cleavage sites by statistical rulersBiochem Biophys Res Commun33810051011PubMedCrossRefGoogle Scholar
  23. Lubec, G, Afjehi-Sadat, L, Yang, JW, John, JP 2005Searching for hypothetical proteins: theory and practice based upon original data and literatureProg Neurobiol7790127PubMedCrossRefGoogle Scholar
  24. Luo, RY, Feng, ZP, Liu, JK 2002Prediction of protein structural class by amino acid and polypeptide compositionEur J Biochem26942194225PubMedCrossRefGoogle Scholar
  25. McGeoch, DJ 1985On the predictive recognition of signal peptide sequencesVirus Res3271286PubMedCrossRefGoogle Scholar
  26. Nakai, K 2000Protein sorting signals and prediction of subcellular localizationAdv Protein Chem54277344PubMedCrossRefGoogle Scholar
  27. Needleman, SB, Wunsch, CD 1970A general method applicable to the search for similarities in the amino acid sequence of two proteinsJ Mol Biol48443453PubMedCrossRefGoogle Scholar
  28. Nielsen, H, Engelbrecht, J, Brunak, S, von Heijne, G 1997Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sitesProtein Eng1016PubMedCrossRefGoogle Scholar
  29. Nielsen, H, Krogh, A 1998Prediction of signal peptides and signal anchors by a hidden Markov modelIntell Syst Mol Biol6122130Google Scholar
  30. Niu, B, Cai, YD, Lu, WC, Zheng, GY, Chou, KC 2006Predicting protein structural class with AdaBoost learnerProtein Peptide Lett13489492CrossRefGoogle Scholar
  31. Schneider, G, Rohlk, S, Wrede, P 1993Analysis of cleavage-site patterns in protein precusor sequences with a perceptron-type neural networkBiochem Biophys Res Commun194951959PubMedCrossRefGoogle Scholar
  32. Schneider, G, Wrede, P 1993Signal analysis of protein targeting sequencesProtein Seq Data Anal5227236Google Scholar
  33. Sun, XD, Huang, RB 2006Prediction of protein structural classes using support vector machinesAmino Acids30469475PubMedCrossRefGoogle Scholar
  34. von Heijne, G 1986A new method for predicting signal sequence cleavage sitesNucleic Acids Res1446834690PubMedCrossRefGoogle Scholar
  35. Wang, M, Yang, J, Chou, KC 2005aUsing string kernel to predict signal peptide cleavage site based on subsite coupling modelAmino Acids28395402Erratum, ibid. 2005, 29: 301CrossRefGoogle Scholar
  36. Wang, M, Yang, J, Xu, ZJ, Chou, KC 2005bSLLE for predicting membrane protein typesJ Theor Biol232715CrossRefGoogle Scholar
  37. Wen Z, Li M, Li Y, Guo Y, Wang K (2006) Delaunay triangulation with partial least squares projection to latent structures: a model for G-protein coupled receptors classification and fast structure recognition. Amino Acids (in press)Google Scholar
  38. Xiao, X, Shao, S, Ding, Y, Huang, Z, Huang, Y, Chou, KC 2005Using complexity measure factor to predict protein subcellular locationAmino Acids285761PubMedCrossRefGoogle Scholar
  39. Xiao, X, Shao, SH, Ding, YS, Huang, ZD, Chou, KC 2006aUsing cellular automata images and pseudo amino acid composition to predict protein sub-cellular locationAmino Acids304954CrossRefGoogle Scholar
  40. Xiao, X, Shao, SH, Huang, ZD, Chou, KC 2006bUsing pseudo amino acid composition to predict protein structural classes: approached with complexity measure factorJ Comput Chem27478482CrossRefGoogle Scholar
  41. Zhang, SW, Pan, Q, Zhang, HC, Shao, ZC, Shi, JY 2006Prediction protein homo-oligomer types by pseudo amino acid composition: approached with an improved feature extraction and naive Bayes feature fusionAmino Acids30461468PubMedCrossRefGoogle Scholar
  42. Zhou, GP 1998An intriguing controversy over protein structural class predictionJ Protein Chem17729738PubMedCrossRefGoogle Scholar
  43. Zhou, GP, Doctor, K 2003Subcellular location prediction of apoptosis proteinsProteins Struct Funct Genet504448PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • D.-Q. Liu
    • 1
  • H. Liu
    • 1
  • H.-B. Shen
    • 1
  • J. Yang
    • 1
  • K.-C. Chou
    • 1
    • 2
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiaotong UniversityShanghaiChina
  2. 2.Gordon Life Science InstituteSan DiegoUSA

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