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Effective 3D protein structure prediction with local adjustment genetic-annealing algorithm

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Abstract

The protein folding problem consists of predicting protein tertiary structure from a given amino acid sequence by minimizing the energy function. The protein folding structure prediction is computationally challenging and has been shown to be NP-hard problem when the 3D off-lattice AB model is employed. In this paper, the local adjustment genetic-annealing (LAGA) algorithm was used to search the ground state of 3D offlattice AB model for protein folding structure. The algorithm included an improved crossover strategy and an improved mutation strategy, where a local adjustment strategy was also used to enhance the searching ability. The experiments were carried out with the Fibonacci sequences. The experimental results demonstrate that the LAGA algorithm appears to have better performance and accuracy compared to the previous methods.

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References

  1. Anfinsen, C.B. 1973. Principles that govern the folding of protein chains. Science 181, 223–227.

    Article  CAS  PubMed  Google Scholar 

  2. Bachmann, M., Arkin, H., Janke, W. 2005. Multicanonical study of coarse-grained off-lattice models for folding heteropolymers. Phys Rev E 71, 031906.

    Article  Google Scholar 

  3. Gatti, C.J., Hughes, R.E. 2009. Optimization of muscle wrapping objects using simulated annealing. Annals of Biomedical Engineering 37, 1342–1347.

    Article  PubMed  Google Scholar 

  4. Irbäck, A., Peterson, C., Potthast, F., Sommelius, O. 1997. Local interactions and protein folding: A 3D offlattice approach. J Chem Phys 107, 273–282.

    Article  Google Scholar 

  5. Kernytsky, A., Rost, B. 2009. Using genetic algorithms to select most predictive protein features. Proteins 75, 75–88.

    Article  CAS  PubMed  Google Scholar 

  6. Kim, S.Y., Lee, S.B., Lee, J. 2005. Structure optimization by conformational space annealing in an off-lattice protein model. Phys Rev E 72, 011916.

    Article  Google Scholar 

  7. Lau, K.F., Dill, K.A. 1989. A lattice statistical mechanics model of the conformational and sequence space of proteins. Macromolecules 22, 3986–3997.

    Article  CAS  Google Scholar 

  8. Lecchini-Visintini, A., Lygeros, J., Maciejowski, J. 2007. Simulated annealing: Rigorous finite-time guarantees for optimization on continuous domains. Advances in Neural Information Processing Systems 20, the Twenty-first Annual Conference on Neural Information Processing Systems (NIPS), British Columbia, Canada.

  9. Lee, J., Liwo, A., Scheraga, H.A. 1999. Energy-based de novo protein folding by conformational space annealing and an off-lattice united-residue force field: application to the 10–55 fragment of staphylococcal protein A and to apo calbindin D9K. Proc Natl Acad Sci USA 96, 2025–2030.

    Article  CAS  PubMed  Google Scholar 

  10. Liang, F. 2004. Annealing contour MonteCarlo algorithm for structure optimization in an off-lattice protein model. J Chem Phys 120, 6756–6763.

    Article  CAS  PubMed  Google Scholar 

  11. Li, B., Wang, L. 2007. A hybrid quantum-inspired genetic algorithm for multi-objective flow shop scheduling. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics 37, 576–591.

    Article  Google Scholar 

  12. Lin, X.L., Zhu, H.B. 2008. Structure optimization by an improved tabu search in the AB off-lattice protein model. 1st International Conference on Intelligent Networks and Intelligent Systems, Wuhan, China.

  13. Liu, J.F., Huang, W.Q. 2007. Quasi-physical algorithm of an off-lattice model for protein folding problem. Journal of Computer Science and Technology 22, 569–574.

    Article  Google Scholar 

  14. Liu, X., Yu, S. 2006. A genetic algorithm with fast local adjustment. Chinese Journal of Computers 29, 100–105 (in Chinese).

    Google Scholar 

  15. Liu, J., Wang, L.H., He, L.L., Shi, F. 2005. Analysis of toy model for protein folding based on particle swarm optimization algorithm. ICNC 3, 636–645.

    Google Scholar 

  16. Shao, P.F., Wan, C.P. 2007. Genetic-annealing algorithm for global optimization problems. Computer Engineering and Applications 43, 62–65 (in Chinese).

    Google Scholar 

  17. Stillinger, F.H. 1995. Collective aspects of protein folding illustrated by a toy model. Phys Rev E52, 2872–2877.

    Google Scholar 

  18. Stillinger, F.H., Head-Gordon, T., Hirshfel, C.L. 1993. Toy model for protein folding. Phys Rev E48, 1469–1477.

    Google Scholar 

  19. Zhang, X.L., Lin, X.L. 2006a. Effective protein folding prediction using an improved genetic-annealing algorithm. The 19th Australian Joint Conference on Artificial Intelligence (AI 2006). LNCS 4304, Springer-Verlag, Hobart, 1196–1200.

    Google Scholar 

  20. Zhang, X.L., Lin, X.L. 2006b. Effective protein folding prediction based on genetic-annealing algorithm in toy model. 2006 Workshop on Intelligent Computing Bioinformatics of CAS, 21–26.

  21. Zhang, X.L., Lin, X. L., Wan, C. P., Li, T.T. 2007. Genetic-annealing algorithm for 3D off-lattice protein folding model. The 2nd BioDM Workshop on Data Mining for Biomedical Applications, PAKDD Workshops, 186–193.

  22. Zhu, H.B., Pu, C.D., Lin, X.L. 2009. Protein structure prediction with EPSO in toy model. 2009 Second International Conference on Intelligent Networks and Intelligent Systems.

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Correspondence to Xiao-Long Zhang.

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Zhang, XL., Lin, XL. Effective 3D protein structure prediction with local adjustment genetic-annealing algorithm. Interdiscip Sci Comput Life Sci 2, 256–262 (2010). https://doi.org/10.1007/s12539-010-0033-x

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  • DOI: https://doi.org/10.1007/s12539-010-0033-x

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