Efficient Computation of Fitness Function by Pruning in Hydrophobic-Hydrophilic Model

  • Md. Tamjidul Hoque
  • Madhu Chetty
  • Laurence S. Dooley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3745)


The use of Genetic Algorithms in a 2D Hydrophobic-Hydrophilic (HP) model in protein folding prediction application requires frequent fitness function computations. While the fitness computation is linear, the overhead incurred is significant with respect to the protein folding prediction problem. Any reduction in the computational cost will therefore assist in more efficiently searching the enormous solution space for protein folding prediction. This paper proposes a novel pruning strategy that exploits the inherent properties of the HP model and guarantee reduction of the computational complexity during an ordered traversal of the amino acid chain sequences for fitness computation, truncating the sequence by at least one residue.


Fitness Function Monte Carlo Hydrophobic Residue Fitness Computation Pruning Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Allen, et al.: Blue Gene: A vision for protein science using a petaflop supercomputer. IBM System Journal 40 (2) (2001) Google Scholar
  2. 2.
    Gary, B.F., David, W.C. (eds.): Evolutionary Computation in Bioinformatics. Elsevier Science, Amsterdam (2003)Google Scholar
  3. 3.
    Lathrop, R.H.: Protein Structure Prediction (1998),
  4. 4.
    Beutler, T.C., Dill, K.A.: A fast conformational search strategy for finding low energy structures of model proteins. Protein Science (1996)Google Scholar
  5. 5.
    Unger, R., Moult, J.: Genetic Algorithm for Protein Folding Simulations. J. Mol. Biol. 231, 75–81 (1993)CrossRefGoogle Scholar
  6. 6.
    Dill, K.A., Fiebig, K.M., Chan, H.S.: Cooperativity in Protein-Folding Kinetics. Proceedings of the National Academy of Sciences USA, Biophysics, 90, 1942–1946 (1993)Google Scholar
  7. 7.
    Toma, L., Toma, S.: Contact interactions method: A new algorithm for protein folding simulations. Protein Science 5, 147–153 (1996)CrossRefGoogle Scholar
  8. 8.
    Backofen, R.: Using Constraint Programming for Lattice Protein Folding (1998),
  9. 9.
    Dill, K.A.: Theory for the Folding and Stability of Globular Proteins. Biochemistry 24, 1501 (1985)CrossRefGoogle Scholar
  10. 10.
    Berger, B., Leighton, T.: Protein Folding in the Hydrophobic-Hydrophilic (HP) model is NP-Complete. In: ACM Proceedings of the second annual international conference on Computational molecular biology (1998)Google Scholar
  11. 11.
    Hoque, M.T., Chetty, M., Dooley, L.S.: An Efficient Algorithm for Computing the Fitness Function of a Hydrophobic-Hydrophilic Model. In: 4th International Conference on Hybrid Intelligent Systems (HIS 2004), pp. 285–290 (2004) ISBN 0-7695-2291-2Google Scholar
  12. 12.
    Hoque M.T., Chetty, M. and Dooley L.S.: Partially Computed Fitness Function Based Genetic Algorithm for Hydrophobic-Hydrophilic Model. 4th International Conference on Hybrid Intelligent Systems (HIS 2004), pp. 291-296, ISBN 0-7695-2291-2 Google Scholar
  13. 13.
    König, R., Dandekar, T.: Refined Genetic Algorithm Simulation to Model Proteins. Journal of Molecular Modeling (1999)Google Scholar
  14. 14.
    Takahashi, O., Kita, H., Kobayashi, S.: Protein Folding by A Hierarchical Genetic Algorithm. In: 4th Int. Symp. On Artificial Life and Robotics, AROB (1999)Google Scholar
  15. 15.
    Voelz, V.: Zipping as a fast conformational search strategy for protein folding (2004),
  16. 16.
    Santos, E.E., Santos, E.J.: Effective and Efficient Caching in Genetic Algorithms. In: International Journal on Artificial Intelligence Tools, Worlds Scientific Publishing Company, Singapore (2000)Google Scholar
  17. 17.
    Santos, E.E., Santos, E.J.: Reducing the Computational Load of Energy Evaluations for Protein Folding. In: 4th IEEE Symp. on BIBE (2004)Google Scholar
  18. 18.
    Newman, A.: A new algorithm for protein folding in the HP model. In: Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete Algorithms (2002)Google Scholar
  19. 19.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Md. Tamjidul Hoque
    • 1
  • Madhu Chetty
    • 1
  • Laurence S. Dooley
    • 1
  1. 1.Gippsland School of Information TechnologyMonash UniversityChurchillAustralia

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