Statistical approaches for investigating silk properties

Abstract

Amino acid repeats or motifs have engendered interest because of their significance for protein physical characteristics as well as folding properties. Spider dragline silk proteins are unique because they are composed of long repetitive sections and relatively short non-repetitive sections that are known to interact to generate the very peculiar mechanical and solubility properties of silk. Computational analysis compared with in vitro measurements suggest that the silks achieve their unique pattern of extreme solubility inside the spider glands/complete insolubility outside by correlating their repetitive hydrophobic regions through a type of stochastic resonance, generated by the addition of the non-repetitive sequences to a basically periodic hydrophobicity pattern.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    Scheibel T (2004) Microb Cell Factories 3:14

    Article  Google Scholar 

  2. 2.

    Gosline JM, Guerette PA, Ortlepp CS, Savage KN (1999) J. Exp. Biol. 202(23):3295

    PubMed  Google Scholar 

  3. 3.

    Hijirida DH, Do KG, Michal C, Wong S, Zax D, Jelinski LW (1996) Biophys. J. 71:3442

    PubMed  Google Scholar 

  4. 4.

    Dicko C, Knight D, Kenney JM, Vollrath F (2004) Biomacromolecules 5:758

    Article  PubMed  Google Scholar 

  5. 5.

    Hronska M, van Beek JD, Williamson PT, Vollrath F, Meier BH (2004) Biomacromolecules 5:834

    Article  PubMed  Google Scholar 

  6. 6.

    Kenney JM, Knight D, Wise MJ, Vollrath F (2002) Eur. J. Biochem. 269:4159

    Article  PubMed  Google Scholar 

  7. 7.

    Sponner A, Unger E, Grosse F, Weisshart K (2004) Biomacromolecules 5:840

    Article  PubMed  Google Scholar 

  8. 8.

    Huemmerich D, Helsen CW, Oschmann J, Rudolph R, Scheibel T (2004) Biochemistry 43:13604

    PubMed  Article  Google Scholar 

  9. 9.

    Uversky VN (2002) Protein Sci. 11:739

    Article  PubMed  Google Scholar 

  10. 10.

    Eisenberg D, Weiss RM, Terwilliger TC (1984) Proc. Natl. Acad. Sci. USA 81:140

    Article  ADS  Google Scholar 

  11. 11.

    Murray KB, Gorse D, Thornton JM (2002) J. Mol. Biol. 316:341

    Article  PubMed  Google Scholar 

  12. 12.

    Penel S, Morrison RG, Mortishire-Smith RJ, Doig AJ (1999) J. Mol. Biol. 293:1211

    Article  PubMed  Google Scholar 

  13. 13.

    Rackovsky S (1998) Proc. Natl. Acad. Sci. USA 95:8580

    Article  ADS  Google Scholar 

  14. 14.

    Stott K, Blackburn JM, Butler PJG, Perutz M (1995) Proc. Natl. Acad. Sci. USA 92:6509

    Article  ADS  Google Scholar 

  15. 15.

    Brillinger D (1981) Time Series: Data Analysis and Theory. Holden-Day, New York

    Google Scholar 

  16. 16.

    Miyazawa S, Jernigen RL (1985) Macromolecules 18:534

    Article  ADS  Google Scholar 

  17. 17.

    Hijirida DH, Do KG, Michal C, Wong S, Zax D, Jelinski LW (1996) Biophys. J. 71:3442

    PubMed  Google Scholar 

  18. 18.

    Paulsson J, Otto G, Berg OG, Ehrenberg M (2000) Proc. Natl. Acad. Sci. USA 97:7148

    Article  ADS  Google Scholar 

  19. 19.

    Alcor D, Croquette V, Jullien L, Lemarchand A (2004) Proc. Natl. Acad. Sci. USA 101:8276

    Article  ADS  Google Scholar 

  20. 20.

    Adair RK (2003) Proc. Natl. Acad. Sci. USA 100:12099

    Article  ADS  Google Scholar 

  21. 21.

    Hasty J, Collins JJ (2001) Nature 411:30

    Article  PubMed  ADS  Google Scholar 

  22. 22.

    Gammaitoni L, Hänggi P, Jung P, Marchesoni F (1998) Rev. Mod. Phys. 70:223

    Article  ADS  Google Scholar 

  23. 23.

    Carrillo O, Santos MA, Garcia-Ojalvo J, Sancho JM (2004) Europhys. Lett. 65:452

    Article  ADS  Google Scholar 

  24. 24.

    Chiti F, Calamai M, Taddei N, Stefani M, Ramponi G, Dobson CM (2002) Proc. Natl. Acad. Sci. USA 99:16419

    Article  ADS  Google Scholar 

  25. 25.

    Norusis MJ (1985) SPSS-X Advanced Statistics Guide. McGraw-Hill, New York

    Google Scholar 

  26. 26.

    Zbilut JP, Mitchell JC, Giuliani A, Colosimo A, Marwan N, Webber CL (2004) Physica A 343:348

    Article  ADS  Google Scholar 

  27. 27.

    Dicko C, Vollrath F, Kenney JM (2004) Biomacromolecules 5:704

    Article  PubMed  Google Scholar 

  28. 28.

    J.P. Zbilut, T. Scheibel, D. Huemmerich, C.L. Webber Jr., M. Colafranceschi, A. Giuliani, Phys. Lett. A (2005) DOI: 10.1016/j.physleta.2005.07.072

  29. 29.

    Linding R, Jensen LJ, Diella F, Bork P, Gibson TJ, Russell RB (2003) Structure 11:1453, available http://us.expasy.org/tools/http://dis.embl.de/ accessed 2005 April 2

    Article  PubMed  Google Scholar 

  30. 30.

    Huemmerich D, Scheibel T, Vollrath F, Cohen S, Gat U, Ittah S (2004) Curr. Biol. 14:2070

    Article  PubMed  Google Scholar 

  31. 31.

    Sinha N, Nussinov R (2001) Proc. Natl. Acad. Sci. USA 98:3139

    Article  ADS  Google Scholar 

  32. 32.

    Zbilut JP, Colosimo A, Conti F, Colafranceschi M, Manetti C, Valerio MC, Webber Jr CL, Giulianii A (2003) Biophys. J. 85:3544

    PubMed  Article  Google Scholar 

  33. 33.

    Valerio MC, Colosimo A, Conti F, Giuliani A, Grottesi A, Manetti C, Zbilut JP (2005) Proteins 58:110

    Article  PubMed  Google Scholar 

  34. 34.

    Buchner J, Kiefhaber T (eds) (2005) Handbook of Protein Folding, vol II. Wiley-VCH, Weinheim pp 193–249

    Google Scholar 

  35. 35.

    Scheibel T (2004) J. Mol. Neurosci. 23:13

    Article  PubMed  Google Scholar 

  36. 36.

    Eckmann JP, Kamphorst SO, Ruelle D (1987) Europhys. Lett. 4:324

    Article  Google Scholar 

  37. 37.

    Zbilut JP, Webber CL (1992) Phys. Lett. A 171:199

    Article  ADS  Google Scholar 

  38. 38.

    Webber CL, Zbilut JP (1994) J. Appl. Physiol. 76:965

    PubMed  Google Scholar 

  39. 39.

    Marwan N, Thiel M, Nowaczyk NR (2002) Nonlinear Proc. Geophys. 9:325

    ADS  Article  Google Scholar 

  40. 40.

    Marwan N, Wessel N, Meyerfeldt U, Schirdewan A, Kurths J (2002) Phys. Rev. E 66:026702-1

    Article  ADS  Google Scholar 

  41. 41.

    Christie OHJ (1995) Chemometr. Intell. Lab. 29:177

    Article  Google Scholar 

  42. 42.

    Plaxco KW, Simons KT, Baker D (1998) J. Mol. Biol. 277:985

    Article  PubMed  Google Scholar 

  43. 43.

    Ivankov DN, Garbuzynskiy SO, Alm E, Plaxco KW, Baker D, Finkelstein AV (2003) Protein Sci. 12:2057

    Article  PubMed  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to J.P. Zbilut.

Additional information

PACS

87.14.Ee; 87.15.Cc; 87.15.He; 02.50.Ey; 05.40.Ca

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Zbilut, J., Scheibel, T., Huemmerich, D. et al. Statistical approaches for investigating silk properties. Appl. Phys. A 82, 243–251 (2006). https://doi.org/10.1007/s00339-005-3429-4

Download citation

Keywords

  • Stochastic Resonance
  • Recurrence Plot
  • Spider Silk
  • Dragline Silk
  • Laminar Area