Protein Structure Prediction in 2D HP Lattice Model Using Differential Evolutionary Algorithm

  • Nanda Dulal Jana
  • Jaya Sil
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 132)


Protein Structure Prediction (PSP) is a challenging problem in bioinformatics and computational biology research for its immense scope of application in drug design, disease prediction, name a few. Developing a suitable optimization technique for predicting the structure of proteins has been addressed in the paper, using Differential Evolutionary (DE) algorithm applied in the square 2D HP lattice model. In the work, we concentrate on handling infeasible solutions and modify control parameters like population size (NP), scale factor (F), crossover ratio (CR) and mutation strategy of the DE algorithm to improve its performance in PSP problem. The proposed method is compared with the existing methods using benchmark sequence of protein databases, showing very promising and effective performance in PSP problem.


Differential Evolutionary Lattice Model Differential Evolutionary Algorithm Protein Structure Prediction Mutation 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Unger, R.: The genetic algorithm approach to protein structure prediction. Structure and Bonding 110, 153–175 (2004)Google Scholar
  2. 2.
    Berger, B., Leight, T.: Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete. Journal of Computational Biology 5(1), 27–40 (1998)CrossRefGoogle Scholar
  3. 3.
    Unger, R., Moult, J.: A Genetic Algorithm for Three Dimensional Protein Folding Simulations. In: Proceedings of the 5th Annual International Conference on Genetic Algorithms, pp. 581–588 (1993)Google Scholar
  4. 4.
    Pedersen, J.T., Moult, J.: Protein Folding Simulations with Genetic Algorithms and a Detailed Molecular Description. J. Mol. Biol. 269, 240–259 (1997)CrossRefGoogle Scholar
  5. 5.
    Bitello, R., Lopes, H.S.: A differential evolution approach for protein folding. In: Proc. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 1–5 (2006)Google Scholar
  6. 6.
    Dill, K.A.: Theory for the folding and stability of globular proteins. Biochemistry 24, 1501 (1985)CrossRefGoogle Scholar
  7. 7.
    Storn, R.M., Price, K.V.: Differential Evolution- a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012, International Computer Science Institute, Berkeley, USA (1995)Google Scholar
  8. 8.
    Storn, R.M., Price, K.V.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Storn, R.M., Price, K.V., Lampinen, J.A.: Differential Evolution – A Practical Approach to Global Optimization. Springer, Berlin (2005)zbMATHGoogle Scholar
  10. 10.
    Das, S., Suganthan, P.N.: Differential Evolution: A survey of the state-of –the-art. IEEE Transaction on Evolutionary Computation 15(1) (2011)Google Scholar
  11. 11.
    Storn, R.: On the usage of differential evolution for function optimization. In: Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 519–524. IEEE, Berkeley (1996)Google Scholar
  12. 12.
    Krasnogor, N., Hart, W.E., Smith, J., Pelta, D.A.: Protein structure prediction with evolutionary algorithms. In: Proc. Int. Genetic and Evolutionary Computation Conf., pp. 1596–1601 (1999)Google Scholar
  13. 13.
    Liu, J., Lampinen, J.: On setting the control parameter of the differential method. In: Pro. 8th Int., Conf. Soft Computing (MENDEL 2002), pp. 11–18 (2002)Google Scholar
  14. 14.
    Unger, R., Moult, J.: Genetic Algorithms for protein folding simulations. Journal of Molecular Biology 231(1), 75–81 (1993)CrossRefGoogle Scholar
  15. 15.
    Krasnogor, N., Blackburne, B.P., Burke, E.K., Hirst, J.D.: Multimeme Algorithms for Protein Structure Prediction. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 769–778. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Santos, J., Diéguez, M.: Differential Evolution for Protein Structure Prediction Using the HP Model. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part I. LNCS, vol. 6686, pp. 323–333. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nanda Dulal Jana
    • 1
  • Jaya Sil
    • 2
  1. 1.Department of Information TechnologyNational Institute of TechnologyDurgapurIndia
  2. 2.Department of Computer Science and TechnologyBengal Engineering & Science UniversityIndia

Personalised recommendations