Nothing Special   »   [go: up one dir, main page]

Skip to main content

A New Constraint Solver for 3D Lattices and Its Application to the Protein Folding Problem

  • Conference paper
Logic for Programming, Artificial Intelligence, and Reasoning (LPAR 2005)

Abstract

The paper describes the formalization and implementation of an efficient constraint programming framework operating on 3D crystal lattices. The framework is motivated and applied to address the problem of solving the ab-initio protein structure prediction problem—i.e., predicting the 3D structure of a protein from its amino acid sequence. Experimental results demonstrate that our novel approach offers up to a 3 orders of magnitude of speedup compared to other constraint-based solutions proposed for the problem at hand.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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.

Similar content being viewed by others

References

  1. Agarwala, R., et al.: Local rules for protein folding on a triangular lattice and generalized hydrophobicity in the HP model. J. Computational Biology, 275–296 (1997)

    Google Scholar 

  2. Apt, K.R.: Principles of constraint programming. Cambridge University press, Cambridge (2003)

    Book  MATH  Google Scholar 

  3. Backofen, R.: The protein structure prediction problem: A constraint optimization approach using a new lower bound. Constraints 6(2–3), 223–255 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  4. Backofen, R., Will, S.: A Constraint-Based Approach to Structure Prediction for Simplified Protein Models that Outperforms Other Existing Methods. In: Palamidessi, C. (ed.) ICLP 2003. LNCS, vol. 2916, pp. 49–71. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  5. Berman, H.M., et al.: The Protein Data Bank. Nucleic Acids Research 28, 235–242 (2000)

    Article  Google Scholar 

  6. Berrera, M., Molinari, H., Fogolari, F.: Amino acid empirical contact energy definitions for fold recognition in the space of contact maps. BMC Bioinformatics 4(8) (2003)

    Google Scholar 

  7. Center for Computational Materials Science, Naval Research Labs, Crystal Lattice Structures, http://cst-www.nrl.navy.mil/lattice/

  8. Clark, D., et al.: Protein topology prediction through constraint-based search and the evaluation of topological folding rules. Protein Engineering 4, 752–760 (1991)

    Google Scholar 

  9. Clote, P., Backofen, R.: Computational Molecular Biology. John Wiley & Sons, Chichester (2001)

    Google Scholar 

  10. Crescenzi, P., et al.: On the complexity of protein folding. In: STOC, pp. 597–603 (1998)

    Google Scholar 

  11. Dal Palù, A., Dovier, A., Fogolari, F.: Constraint logic programming approach to protein structure prediction. BMC Bioinformatics 5(186) (2004)

    Google Scholar 

  12. Dal Palù, A., Dovier, A., Pontelli, E.: Heuristics, Optimizations, and Parallelism for Protein Structure Prediction in CLP(FD). In: Proc. of PPDP 2005 (2005)

    Google Scholar 

  13. Forman, S.: Torsion Angle Selection and Emergent Non-local Secondary Structure in Protein Structure Prediction. PhD thesis, U. of Iowa (2001)

    Google Scholar 

  14. Greenberg, H.J., et al.: Opportunities for Combinatorial Optimization in Computational Biology. INFORMS Journal of Computing (2003)

    Google Scholar 

  15. Hart, W., Newman, A.: The computational complexity of protein structure prediction in simple lattice models. CRC Press, Boca Raton (2003) (to appear)

    Google Scholar 

  16. Krippahl, L., Barahona, P.: Applying Constraint Programming to Protein Structure Determination. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 289–302. Springer, Heidelberg (1999)

    Google Scholar 

  17. Raghunathan, G., Jernigan, R.L.: Ideal architecture of residue packing and its observation in protein structures. Protein Science 6, 2072–2083 (1997)

    Article  Google Scholar 

  18. Rodosek, R.: A Constraint-based Approach for Deriving 3-D Structures of Cyclic Polypeptides. Constraints 6(2-3), 257–270 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  19. Rost, B.: Protein Secondary Structure Prediction Continues to Rise. J. Struct. Biol. 134 (2001)

    Google Scholar 

  20. Skolnick, J., et al.: Reduced models of proteins and applications. Polymer 45, 511–524 (2004)

    Article  Google Scholar 

  21. Toma, L., Toma, S.: Folding simulation of protein models on the structure-based cubo-octahedral lattice with contact interactions algorithm. Protein Science 8, 196–202 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dal Palù, A., Dovier, A., Pontelli, E. (2005). A New Constraint Solver for 3D Lattices and Its Application to the Protein Folding Problem. In: Sutcliffe, G., Voronkov, A. (eds) Logic for Programming, Artificial Intelligence, and Reasoning. LPAR 2005. Lecture Notes in Computer Science(), vol 3835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11591191_5

Download citation

  • DOI: https://doi.org/10.1007/11591191_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30553-8

  • Online ISBN: 978-3-540-31650-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics