Protein Decoy Generation Using Branch and Bound with Efficient Bounding

  • Martin Paluszewski
  • Pawel Winter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5251)

Abstract

We propose a new discrete protein structure model (using a modified face-centered cubic lattice). A novel branch and bound algorithm for finding global minimum structures in this model is suggested. The objective energy function is very simple as it depends on the predicted half-sphere exposure numbers of C α -atoms. Bounding and branching also exploit predicted secondary structures and expected radius of gyration. The algorithm is fast and is able to generate the decoy set in less than 48 hours on all proteins tested.

Despite the simplicity of the model and the energy function, many of the lowest energy structures, using exact measures, are near the native structures (in terms of RMSD). As expected, when using predicted measures, the fraction of good decoys decreases, but in all cases tested, we obtained structures within 6 Å RMSD in a set of low-energy decoys. To the best of our knowledge, this is the first de novo branch and bound algorithm for protein decoy generation that only depends on such one-dimensional predictable measures. Another important advantage of the branch and bound approach is that the algorithm searches through the entire conformational space. Contrary to search heuristics, like Monte Carlo simulation or tabu search, the problem of escaping local minima is indirectly solved by the branch and bound algorithm when good lower bounds can be obtained.

Keywords

Univer EBBA 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Backofen, R., Will, S.: A constraint-based approach to fast and exact structure prediction in three-dimensional protein models. Constraints 11(1), 5–30 (2006)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Boutonnet, N.S., Kajava, A.V., Rooman, M.J.: Structural classification of alphabetabeta and betabetaalpha supersecondary structure units in proteins. Proteins 30(2), 193–212 (1998)CrossRefGoogle Scholar
  3. 3.
    Chothia, C., Lesk, A.M.: The relation between the divergence of sequence and structure in proteins. The EMBO Journal 5, 823–826 (1986)Google Scholar
  4. 4.
    Fain, B., Levitt, M.: A novel method for sampling alpha-helical protein backbones. Journal of Molecular Biology 305, 191–201 (2001)CrossRefGoogle Scholar
  5. 5.
    Hamelryck, T.: An amino acid has two sides: a new 2D measure provides a different view of solvent exposure. Proteins 59(1), 38–48 (2005)CrossRefGoogle Scholar
  6. 6.
    Hamelryck, T., Kent, J.T., Krogh, A.: Sampling realistic protein conformations using local structural bias. PLoS Computational Biology 2(9), 1121–1133 (2006)CrossRefGoogle Scholar
  7. 7.
    Kinjo, A.R., Nishikawa, K.: Recoverable one-dimensional encoding of three-dimensional protein structures. Bioinformatics 21(10), 2167–2170 (2005)CrossRefGoogle Scholar
  8. 8.
    Kolodny, R., Levitt, M.: Protein decoy assembly using short fragments under geometric constraints. Biopolymers 68(3), 278–285 (2003)CrossRefGoogle Scholar
  9. 9.
    Maranas, C.D., Floudas, C.A.: A deterministic global optimization approach for molecular structure determination. J. Chem. Phys. 100, 1247–1261 (1994)CrossRefGoogle Scholar
  10. 10.
    McGuffin, L.J., Bryson, K., Jones, D.T.: The PSIPRED protein structure prediction server. Bioinformatics 16, 404–405 (2000)CrossRefGoogle Scholar
  11. 11.
    Palu, A.D., Dovier, A., Fogolari, F.: Constraint logic programming approach to protein structure prediction. BMC Bioinformatics 5(186) (2004)Google Scholar
  12. 12.
    Paluszewski, M., Hamelryck, T., Winter, P.: Reconstructing protein structure from solvent exposure using tabu search. Algorithms for Molecular Biology 1 (2006)Google Scholar
  13. 13.
    Paluszewski, M., Winter, P.: EBBA: Efficient branch and bound algorithm for protein decoy generation, Department of Computer Science, Univ. of Copenhagen, vol. 08(08) (2008)Google Scholar
  14. 14.
    Pollastri, G., Baldi, P., Fariselli, P., Casadio, R.: Prediction of coordination number and relative solvent accessibility in proteins. Proteins 47(2), 142–153 (2002)CrossRefGoogle Scholar
  15. 15.
    Simons, K.T., Kooperberg, C., Huang, E., Baker, D.: Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 268(1), 209–225 (1997)CrossRefGoogle Scholar
  16. 16.
    Skolnick, J., Kolinski, A., Ortiz, A.R.: MONSSTER: A method for folding globular proteins with a small number of distance restraints. J. Mol. Biol. 265, 217–241 (1997)CrossRefGoogle Scholar
  17. 17.
    Standley, D.M., Eyrich, V.A., Felts, A.K., Friesner, R.A., McDermott, A.E.: A branch and bound algorithm for protein structure refinement from sparse nmr data sets. J. Mol. Biol. 285, 1961–1710 (1999)Google Scholar
  18. 18.
    Sun, Z., Jiang, B.: Patterns and conformations of commonly occurring supersecondary structures (basic motifs) in protein data bank. J. Protein Chem. 15(7), 675–690 (1996)CrossRefGoogle Scholar
  19. 19.
    Vilhjalmsson, B., Hamelryck, T.: Predicting a New Type of Solvent Exposure. In: ECCB, Computational Biology Madrid 2005, P-C35, Poster (2005)Google Scholar
  20. 20.
    Wolsey, L.A.: Integer Programming. Wiley-Interscience, Chichester (1998)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Martin Paluszewski
    • 1
  • Pawel Winter
    • 1
  1. 1.Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark

Personalised recommendations