An Evolutionary Model Based on Hill-Climbing Search Operators for Protein Structure Prediction

  • Camelia Chira
  • Dragos Horvath
  • Dumitru Dumitrescu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6023)

Abstract

The prediction of a minimum-energy protein structure from its amino-acid sequence represents one of the most important and challenging problems in computational biology. A new evolutionary model based on hill-climbing genetic operators is proposed to address the hydrophobic - polar model of the protein folding problem. The introduced model ensures an efficient exploration of the search space by implementing a problem-specific crossover operator and enforcing an explicit diversification stage during the evolution. The mutation operator engaged in the proposed model refers to the pull-move operation by which a single residue is moved diagonally causing the potential transition of connecting residues in the same direction in order to maintain a valid protein configuration. Both crossover and mutation are applied using a steepest-ascent hill-climbing approach. The resulting evolutionary algorithm with hill-climbing operators is successfully applied to the protein structure prediction problem for a set of difficult bidimensional instances from lattice models.

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References

  1. 1.
    Berenboym, I., Avigal, M.: Genetic algorithms with local search optimization for protein structure prediction problem. In: GECCO 2008, pp. 1097–1098 (2008)Google Scholar
  2. 2.
    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
  3. 3.
    Crescenzi, P., Goldman, D., Papadimitriou, C.H., Piccolboni, A., Yannakakis, M.: On the Complexity of Protein Folding. Journal of Computational Biology 50, 423–466 (1998)CrossRefGoogle Scholar
  4. 4.
    Dill, K.A.: Theory for the folding and stability of globular proteins. Biochemistry 24(6), 1501–1509 (1985)CrossRefGoogle Scholar
  5. 5.
    Hart, W., Newman, A.: Protein Structure Prediction with Lattice Models. In: Handbook of Computational Molecular Biology. Chapman & Hall CRC Computer and Information Science Series (2006)Google Scholar
  6. 6.
    Hsu, H.P., Mehra, V., Nadler, W., Grassberger, P.: Growth algorithms for lattice heteropolymers at low temperatures. J. Chem. Phys. 118(1), 444–451 (2003)CrossRefGoogle Scholar
  7. 7.
    Khimasia, M.M., Coveney, P.V.: Protein structure prediction as a hard optimization problem: the genetic algorithm approach. Molecular Simulation 19, 205–226 (1997)CrossRefGoogle Scholar
  8. 8.
    Krasnogor, N., Blackburnem, B., Hirst, J.D., Burke, E.K.: 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
  9. 9.
    Lesh, N., Mitzenmacher, M., Whitesides, S.: A complete and effective move set for simplified protein folding. In: RECOMB 2003: Proceedings of the seventh annual international conference on Research in computational molecular biology, pp. 188–195. ACM, New York (2003)CrossRefGoogle Scholar
  10. 10.
    Lozano, M., Herrera, F., Krasnogor, N., Molina, D.: Real-coded memetic algorithms with crossover hill-climbing. Evol. Comput. 12(3), 273–302 (2004)CrossRefGoogle Scholar
  11. 11.
    Shmygelska, A., Hernandez, R., Hoos, H.H.: An Ant Colony Algorithm for the 2D HP Protein Folding Problem. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 40–53. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Song, J., Cheng, J., Zheng, T., Mao, J.: A Novel Genetic Algorithm for HP Model Protein Folding. In: PDCAT 2005: Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies, pp. 935–937. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar
  13. 13.
    Unger, R., Moult, J.: Genetic algorithms for protein folding simulations. J. Molec. Biol. 231, 75–81 (1993)CrossRefGoogle Scholar
  14. 14.
    Zhao, X.: Advances on protein folding simulations based on the lattice HP models with natural computing. Appl. Soft. Comput. 8(2), 1029–1040 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Camelia Chira
    • 1
  • Dragos Horvath
    • 2
  • Dumitru Dumitrescu
    • 1
  1. 1.Babes-Bolyai UniversityCluj-NapocaRomania
  2. 2.Laboratoire d’InfochimieUMR 7177, University StrasbourgFrance

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