Skip to main content

Protein Folding, the Levinthal Paradox and Rapidly Mixing Markov Chains

  • Conference paper
  • First Online:
Automata, Languages and Programming

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1644))

Abstract

In [20,21], A. Šali, E. Shakhnovich and M. Karplus modeled protein folding using a 27-bead heteropolymer on a cubic lattice with normally distributed contact energies; i.e.

$$ E = \sum\limits_{1 \leqslant i < j \leqslant 27} {B_{ij} \delta \left( {r_{i,j} } \right)} $$

where Bi,j is normally distributed with mean -2 and standard variation 1, ri,j is Euclidean distance between residues i, j, and δ(ri,j) = 1 if ri,j = 1 and i, j are not immediate neighbors in the polypeptide chain (i.e. ∣i - j∣ > 1), else 0.

Research supported by Volkswagen-Stiftung

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. C.B. Anfinsen. Principles that govern the folding of protein chains. Science, 181:223–230, 1973.

    Article  Google Scholar 

  2. B. Berger and T. Leighton. Journal of Computational Biology.

    Google Scholar 

  3. H.S. Chan and K.A. Dill. Compact polymers. Macromolecules, 22:4559, 1989.

    Article  Google Scholar 

  4. Hue Sun Chan. Kinetics of protein folding. Nature, 373:664–665, 23 February 1995. Scientific Correspondence: Criticism to [20].

    Article  Google Scholar 

  5. P. Crescenzi, D. Goldman, C. Papadimitriou, A. Piccolboni, and M. Yannakakis. On the complexity of protein folding. Journal of Computational Biology, 5(3):523–566, 1998.

    Article  Google Scholar 

  6. P. Diaconis and D. Stroock. Geometric bounds for eigenvalues of markov chains. Annals of Applied Probability, 1:35–61, 1991.

    Article  MathSciNet  Google Scholar 

  7. W. Feller. An introduction to probability theory and its applications. J.Wiley and Sons, Inc, 1968. Volume 1, Third Edition.

    Google Scholar 

  8. W. Hart and S. Istrail. Fast protein folding in the hydrophobic-hydrophobic model within three-eighths of optimal. In Proceedings of the 27th Annual ACM Symposium on Theory of Computing, Las Vegas, 1995. 157–168.

    Google Scholar 

  9. M. Karplus. Santa Fe, Jan 20-23, 1997.

    Google Scholar 

  10. M. Karplus and E. Shakhnovich. Protein folding: theoretical studies of thermodynamics and dynamics. In T.E. Creighton, editor, Protein Folding, pages 237–296. iW.H. Freeman and Company, New York, 1992.

    Google Scholar 

  11. M. Karplus, A. Šali, and E. Shakhnovich. Kinetics of protein folding. Nature, 373:665, 23 February 1995. Scientific Correspondence: Reply to [4].

    Article  Google Scholar 

  12. J.G. Kemeny and J.L. Snell. Finite Markov Chains. Van Nostrand Company, 1960. 210pages.

    Google Scholar 

  13. C. Levinthal. Are there pathways for protein folding? J. Chim. Phys., 65:44–45, 1968.

    Article  Google Scholar 

  14. N. Madras and A.D. Sokol. Nonergodicity of local, length-conserving monte-carlo algorithms for the self-avoiding walk. J. Stat. Phys., 47:573–595, 1987.

    Article  MathSciNet  Google Scholar 

  15. Y.A. Rozanov. Probability Theory: A Concise Course. Dover Publications, Inc., 1977.

    Google Scholar 

  16. E. Shakhnovich. Theoretical studies of protein-folding thermodynamics and kinetics. Current Opinion in Structural Biology, 7:29–40, 1997.

    Article  Google Scholar 

  17. Alistair Sinclair. Algorithms for random generation and counting: A Markov chain approach. Birkhäuser, 1993. 146 pages.

    Google Scholar 

  18. M. Teeter. An empirical examination of potential energy minimization using the well-determined structure of the protein crambin. Journal of the American Chemical Society, 108:7163–7172, 1986.

    Article  Google Scholar 

  19. M. Teeter. Water-protein interactions: Theory and experiment. Annu. Rev. Biophys. Biophys. Chem., 20:577–600, 1991.

    Article  Google Scholar 

  20. A. Šali, E. Shakhnovich, and M. Karplus. How does a protein fold? Nature, 369:248–251, 19 May 1994. Letters to Nature.

    Article  Google Scholar 

  21. A. Šali, E. Shakhnovich, and M. Karplus. Kinetics of protein folding: A lattice model study of the requirements for folding to the native state. J. Molec. Biol., 235:1614–1636, 1994.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Clote, P. (1999). Protein Folding, the Levinthal Paradox and Rapidly Mixing Markov Chains. In: Wiedermann, J., van Emde Boas, P., Nielsen, M. (eds) Automata, Languages and Programming. Lecture Notes in Computer Science, vol 1644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48523-6_21

Download citation

  • DOI: https://doi.org/10.1007/3-540-48523-6_21

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66224-2

  • Online ISBN: 978-3-540-48523-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics