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Protein structure prediction with the 3D-HP side-chain model using a master–slave parallel genetic algorithm

Abstract

This work presents a master-slave parallel genetic algorithm for the protein folding problem, using the 3D-HP side-chain model (3D-HP-SC). This model is sparsely studied in the literature, although more expressive than other lattice models. The fitness function proposed includes information not only about the free-energy of the conformation, but also compactness of the side-chains. Since there is no benchmark available to date for this model, a set of 15 sequences was used, based on a simpler model. Results show that the parallel GA achieved a good level of efficiency and obtained biologically coherent results, suggesting the adequacy of the methodology. Future work will include new biologically-inspired genetic operators and more experiments to create new benchmarks.

References

  1. Anfinsen CB Principles that govern the folding of protein chains. Science (181)

  2. Armstrong NB Jr, Lopes HS, Lima CRE (2007) Reconfigurable computing for accelerating protein folding simulations. Lect Notes Comput Sci 4419:314–325

    Article  Google Scholar 

  3. Atkins J, Hart WE (1999) On the intractability of protein folding with a finite alphabet. Algorithmica 25(2–3):279–294

    Article  MathSciNet  Google Scholar 

  4. Benítez CMV, Lopes HS (2009) Algoritmo genético aplicado à predição da estrutura de proteínas utilizando o modelo 3D-HP side chain. In: Anais do VII encontro nacional de inteligência artificial (ENIA)

  5. Benítez CMV, Lopes HS (2009) A parallel genetic algorithm for protein folding prediction using the 3DHP side-chain model. In: Proceedings of IEEE congress on evolutionary computation. IEEE Computer Society, Piscataway, pp 1297–1304

    Google Scholar 

  6. Berger B, Leighton FT (1998) Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete. J Comput Biol 5(1):27–40

    Article  Google Scholar 

  7. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) UniProt archive. Nucleic Acids Res 28(1):235–242

    Article  Google Scholar 

  8. Box GE, Hunter WG, Hunter JS (2005) Statistics for experimenters: design, innovation, and discovery, 2nd edn. Wiley, New York

    Google Scholar 

  9. Cantú-Paz E (2000) Efficient and accurate parallel genetic algorithms. Springer, New York

    Google Scholar 

  10. Crescenzi P, Goldman D, Papadimitriou C, Piccolboni A, Yannakakis M (1998) On the complexity of protein folding. J Comput Biol 5(3):423–465

    Article  Google Scholar 

  11. Custódio FL, Barbosa HJC, Dardenne LE (2004) Investigation of the three-dimensional lattice HP protein folding model using a genetic algorithm. Genet Mol Biol 27(4):611–615

    Article  Google Scholar 

  12. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester

    Google Scholar 

  13. Dill KA, Bromberg S, Yue K, Fiebig KM, Yee DP, Thomas PD, Chan HS (1995) Principles of protein folding—a perspective from simple exact models. Protein Sci 4(4):561–602

    Article  Google Scholar 

  14. Gropp W, Lusk E, Thakur R (1999) Using MPI2: advanced features of the message-passing interface. MIT Press, Cambridge

    Google Scholar 

  15. Hembecker F, Lopes HS, Godoy W Jr (2007) Particle swarm optimization for the multidimensional knapsack problem. Lect Notes Comput Sci 4331:358–365

    Article  Google Scholar 

  16. Krasnogor N, Hart WE, Smith J, Pelta DA (1999) Protein structure prediction with evolutionary algorithms. In: Banzhaf D, Eiben G, Honovar J, Smith S (eds) Proceedings of the international genetic and evolutionary computation conference, San Mateo, CA, pp 1596–1601

  17. Leinonen R, Diez FG, Binns D, Fleischmann W, Lopez R, Apweiler R (2004) UniProt archive. Bioinformatics 20(17):3236–3237

    Article  Google Scholar 

  18. Li MS, Klimov DK, Thirumalai D (2002) Folding in lattice models with side chains. Comput Phys Commun 147(1):625–628

    Article  Google Scholar 

  19. Lobo FG, Lima CF, Michalewicz Z (2007) Parameter setting in evolutionary algorithms. Springer, New York

    Book  Google Scholar 

  20. Lopes HS (2008) Evolutionary algorithms for the protein folding problem: a review and current trends. In: Computational intelligence in biomedicine and bioinformatics, vol I. Springer, Heidelberg, pp 297–315

    Chapter  Google Scholar 

  21. Lopes HS, Scapin MP (2005) An enhanced genetic algorithm for protein structure prediction using the 2D hydrophobic-polar model. Lect Notes Comput Sci 3871:238–246

    Article  Google Scholar 

  22. Maruo MH, Lopes HS, Delgado MRB (2005) Self-adapting evolutionary parameters: encoding aspects for combinatorial optimization problems. Lect Notes Comput Sci 3448:154–165

    Article  Google Scholar 

  23. Matsumoto M, Nishimura T (1998) Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans Model Comput Simul 8(1):3–30

    Article  Google Scholar 

  24. Nelson DL, Cox MM (2008) Lehninger principles of biochemistry, 5th edn. Freeman, New York

    Google Scholar 

  25. Scapin MP, Lopes HS (2007) A hybrid genetic algorithm for the protein folding problem using the 2D-HP lattice model. In: Yang A, Shan Y, Bui LT (eds) Success in evolutionary computation. Studies in computational intelligence, vol 92. Springer, Heidelberg, pp 205–224

    Google Scholar 

  26. Song J, Cheng J, Zheng T, Mao J (2005) A novel genetic algorithm for hp model protein folding. In: Proceedings of 6th international conference on parallel and distributed computing applications and technologies. IEEE Computer Society, Washington, pp 935–937

    Google Scholar 

  27. Unger R, Moult J (1993) A genetic algorithm for 3D protein folding simulations. In: Proceedings of the 5th annual international conference on genetic algorithms, pp 581–588

  28. Yue K, Dill KA (1993) Sequence-structure relationships in proteins and copolymers. Phys Rev E 48(3):2267–2278

    Google Scholar 

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Correspondence to César Manuel Vargas Benítez.

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This paper is an extended version of a paper that appeared at ENIA 2009 (The Brazilian Meeting on Artificial Intelligence).

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Benítez, C.M.V., Lopes, H.S. Protein structure prediction with the 3D-HP side-chain model using a master–slave parallel genetic algorithm. J Braz Comput Soc 16, 69–78 (2010). https://doi.org/10.1007/s13173-010-0002-6

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  • DOI: https://doi.org/10.1007/s13173-010-0002-6

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