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A Hybrid Genetic Algorithm for 2D Protein Folding Simulations

by Hamza Turabieh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 3
Year of Publication: 2016
Authors: Hamza Turabieh
10.5120/ijca2016909127

Hamza Turabieh . A Hybrid Genetic Algorithm for 2D Protein Folding Simulations. International Journal of Computer Applications. 139, 3 ( April 2016), 38-43. DOI=10.5120/ijca2016909127

@article{ 10.5120/ijca2016909127,
author = { Hamza Turabieh },
title = { A Hybrid Genetic Algorithm for 2D Protein Folding Simulations },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 139 },
number = { 3 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 38-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume139/number3/24473-2016909127/ },
doi = { 10.5120/ijca2016909127 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:39:59.158526+05:30
%A Hamza Turabieh
%T A Hybrid Genetic Algorithm for 2D Protein Folding Simulations
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 3
%P 38-43
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Protein folding problem is one of the most interesting problem in the medical field, which consists in finding the tertiary structure for a given amino acid sequence of a protein. Protein folding is NP hard problem. In this paper, we hybridized genetic algorithm with a local search algorithm to solve 2D Protein folding problem. This kind of hybridization empower the genetic algorithm exploration and exploitation process. The local search algorithm used is great deluge algorithm, which focus on intensification process. The experiments conducted in this work have shown the good performance of the proposed algorithm compared to similar approaches of the state of the art when dealing with different protein folding optimization problems. In particular, a good tradeoff between search space diversication and intensication is achieved. Possible extensions upon this hybridization are also discussed.

References
  1. Backofen R. and Will S. 2006. A constraint-based approach to fast and exact structure prediction in three-dimensional protein models. Constraints, 11(1):530.
  2. Lau KF. and Dill KA. 1989. lattice statistical mechanics model of the conformation and sequence space of proteins. Macromolecules 22:3986-3997.
  3. Huang C. and Yang X. and He Z. 2010. Protein folding simulations of 2D HP model by the genetic algorithm based on optimal secondary structures, Computational Biology and Chemistry, 34(3), 137-142.
  4. Unger R. and Moult J. 1993. Genetic algorithms for protein folding simulations. Journal of Molecular Biology, 231:75-81.
  5. Unger R., Moult J. 1993. A genetic algorithm for three dimensional protein folding simulations. In Proc of the 5th International Conference on Genetic Algorithms, Morgan Kaufmann Publishers; 581-588.
  6. Patton W., Punch W., and Goldman E. 1995. A standard genetic algorithm approach to native protein conformation prediction. In Proceedings of 6th International Conference on Genetic Algorithms, 574–581.
  7. Krasnogor N., Hart W.E. , Smith J. , and Pelta D.A. 1999. Protein structure prediction with evolutionary algorithms. In W. Banzhaf et al., editors, Proceedings of the GECCO’99, 1596-1601, San Mateo CA.
  8. Jiang, T.Z., Hua, Q., Cui, Shi, G.H., Ma, S.D. 2003. Protein folding simulations of the hydrophilic model by combining tabu search with genetic algorithms. J. Chem. Phys. 119 (8), 4592-4596.
  9. Liang, F.M., Wong, W.H. 2001. Evolutionary Monte Carlo for protein folding simulations. J. Chem. Phys. 115 (7), 3374-3380.
  10. Ramakrishnan R., Ramachandran B., and Pekny J. F. 1997. A dynamic Monte Carlo algorithm for exploration of dense conformational spaces in heteropolymers. Chemical Physics, 106(6):2418-2425.
  11. Shmygelska A. and Holger H. 2003. An Improved Ant Colony Optimisation Algorithm for the 2D HP Protein Folding Problem. In Springer Verlag, editor, In Proceedings of the 16th Canadian Conference on Artificial Intelligence, 400-417.
  12. Shmygelska A. and Holger H. 2005. An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinformatics, 6(30).
  13. Liang F. and Hung W. W. 2001. Evolutionary Monte Carlo for protein folding simulations. Journal of Chemical Physics, 115(7).
  14. Huang C. and Yang X. and He Z. 2010. Protein folding simulations of 2D HP model by the genetic algorithm based on optimal secondary structures, Computational Biology and Chemistry, 34(3), 137-142.
  15. Goldberg, D. and Holland, J. 1988. Genetic algorithms and machine learning. Machine Learning, 3(2):95–99.
  16. Dueck, G. 1993. New Optimization Heuristics. The great deluge algorithm and the record-to-record travel. Journal of Computational Physics 104, 86-92.
  17. Guo Y.Z., Meng, E.M., Wang, Y. 2006. Exploration of two-dimensional hydrophobicpolar lattice model by combining local search with elastic net algorithm. J. Chem. Phys. 125, 154102-154106.
Index Terms

Computer Science
Information Sciences

Keywords

Protein folding Genetic algorithm Great deluge 2D HP Model.

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