Representations for Evolutionary Algorithms Applied to Protein Structure Prediction Problem Using HP Model

  • Paulo H. R. Gabriel
  • Alexandre C. B. Delbem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5676)


Protein structure prediction (PSP) is a computational complex problem. To work with large proteins, simple protein models have been employed to represent the conformation, and evolutionary algorithms (EAs) are usually used to search for adequate solutions. However, the generation of unfeasible conformations may decrease the EA performance. For this reason, this paper presents two alternative representations that reduce the number of improper structures, improving the search process. Both representations have been investigated in terms of initial population in order to start the evolutionary process with promising regions. The results have shown a significant improvement in the fitness values (or, in other words, in solution quality).


Protein Structure Prediction Representation of Evolutionary Algorithms HP Model Cubic Lattice 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Setubal, J.C., Meidanis, J.: Introduction to Computational Molecular Biology. PWS Publishing Company, Boston (1997)Google Scholar
  2. 2.
    De Jong, K.A.: Evolutionary Computation: A Unified Approach. The MIT Press, Cambridge (2006)Google Scholar
  3. 3.
    Dill, K.A.: Theory for the folding and stability of globular proteins. Biochemistry 24(6), 1501–1509 (1985)Google Scholar
  4. 4.
    Khimasia, M.M., Coveney, P.: Protein structure prediction as a hard optimization problem: The genetic algorithm approach. Molecular Simulation 19, 205–226 (1997)CrossRefGoogle Scholar
  5. 5.
    Krasnogor, N., Hart, W.E., Smith, J., Pelta, D.A.: Protein structure prediction with evolutionary algorithms. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, vol. 2, pp. 1596–1601. Morgan Kaufmann, San Francisco (1999)Google Scholar
  6. 6.
    Patton, A.L., Punch III, W.F., Goodman, E.D.: A standard GA approach to native protein conformation prediction. In: Eshelman, L. (ed.) Proceedings of Sixth International Conference on Genetic Algorithms, pp. 574–581. Morgan Kaufmann, San Francisco (1995)Google Scholar
  7. 7.
    Piccolboni, A., Mauri, G.: Application of evolutionary algorithms to protein folding prediction. In: Hao, J.-K., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds.) AE 1997. LNCS, vol. 1363, pp. 123–136. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  8. 8.
    Unger, R., Moult, J.: A genetic algorithm for 3D protein folding simulations. In: Proceedings of Fifth Annual International Conference on Genetic Algorithms, San Francisco, CA, USA, pp. 581–588 (1993)Google Scholar
  9. 9.
    Clote, P., Backofen, R.: Computational Molecular Biology: An Introduction. Wiley Series in Mathematical and Computational Biology. John Wiley & Sons Inc., New York (2000)Google Scholar
  10. 10.
    Cotta, C.: Protein structure prediction using evolutionary algorithms hybridized with backtracking. In: Mira, J., Álvarez, J.R. (eds.) IWANN 2003. LNCS, vol. 2687, pp. 321–328. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Rothlauf, F.: Representation for Genetic and Evolutionary Algorithms. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    de Melo, V.V., Delbem, A.C.B., Pinto Júnior, D.L., Federson, F.M.: Discovering promising regions to help global numerical optimization algorithms. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS, vol. 4827, pp. 72–82. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    de Melo, V.V., Delbem, A.C.B.: On promising regions and optimization effectiveness of continuous and deceptive functions. In: IEEE Congress on Evolutionary Computation, June 2008, pp. 4184–4191 (2008)Google Scholar
  14. 14.
    Hart, W.E., Newman, A.: Protein structure prediction with lattice models. In: Aluru, S. (ed.) Handbook of Molecular Biology. Chapman & Hall/CRC Computer and Information Science Series, pp. 1–24. CRC Press, New York (2006)Google Scholar
  15. 15.
    Berger, B., Leighton, T.: Protein folding in the hydrophobic–hydrophilic (HP) model is NP-complete. Journal of Computational Biology 5(1), 27–40 (1998)CrossRefPubMedGoogle Scholar
  16. 16.
    Crescenzi, P., Goldman, D., Papadimitriou, C., Piccolboni, A., Yannakakis, M.: On the complexity of protein folding. Journal of Computational Biology 5(3), 423–466 (1998)CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Paulo H. R. Gabriel
    • 1
  • Alexandre C. B. Delbem
    • 1
  1. 1.Depto. Sistemas de Computação, ICMC/USPSão Carlos/SPBrazil

Personalised recommendations