3D Protein Structure Prediction with Local Adjust Tabu Search Algorithm

  • Xiaoli Lin
  • Fengli Zhou
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)


The protein folding structure prediction is computationally challenging and has been shown to be NP-hard when the 3D off-lattice AB model is employed. In this paper, the local adjustment tabu search (LATS) algorithm has been used to search the ground state of 3D AB off-lattice model for protein folding structure. A kind of optimization about the neighborhood scale and the annealing mechanism has been presented, where a local adjustment strategy has also been used to enhance the searching ability for the global minimum within theAB off-lattice model. Experimental results demonstrate that the proposed algorithm has better performance in global optimization and can predict 3D protein structure more effectively.


Protein Structure Prediction Tabu Search Algorithm 3D Off-Lattice Model Local Adjustment 


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  1. 1.
    Anfinsen, C.B.: Principles that govern the folding of protein chains. Science 181, 223–227 (1973)CrossRefGoogle Scholar
  2. 2.
    Lopes, H.S.: Evolutionary Algorithms for the Protein Folding Problem: A Review and Current Trends. In: Smolinski, T.G., Milanova, M.G., Hassanien, A.-E. (eds.) Computational Intelligence in Biomedicine and Bioinformatics. SCI, vol. 151, pp. 297–315. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Dill, K.A.: Theory for the folding and stability of globular proteins. Biochemistry 24, 1501–1509 (1985)CrossRefGoogle Scholar
  4. 4.
    Hart, W.E., Newman, A.: Protein structure prediction with lattice models. In: Aluru, S. (ed.) Handbook of Molecular Biology. Computer and Information Science Series, pp. 1–24. Chapman & Hall/CRC Press (2006)Google Scholar
  5. 5.
    Irbäck, A., Peterson, C., Potthast, F., Sommelius, O.: Local interactions and protein folding: A three-dimensional off-lattice approach. J. Chem. Phys. 107, 273–282 (1997)CrossRefGoogle Scholar
  6. 6.
    Stillinger, F.H.: Collective aspects of protein folding illustrated by a toy model. Phys. Rev. E 52, 2872–2877 (1995)CrossRefGoogle Scholar
  7. 7.
    Bachmann, M., Arkin, H., Janke, W.: Multicanonical study of coarse-grained off-lattice models for folding heteropolymers. Phys. Rev. E 71, 31906 (2005)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kim, S.-Y., Lee, S.B., Lee, J.: Structure optimization by conformational space annealing in an off-lattice protein model. Phys. Rev. E 72, 011916 (2005)Google Scholar
  9. 9.
    Hsu, H.-P., Mehra, V., Grassberger, P.: Structure optimization in an off-lattice protein model. Phys. Rev. E 68, 037703 (2003)Google Scholar
  10. 10.
    Zhu, H.B., Pu, C.D., Lin, X.L.: Protein structure prediction with EPSO in toy model. In: 2009 Second International Conference on Intelligent Networks and Intelligent Systems (2009)Google Scholar
  11. 11.
    Lin, X.L., Zhu, H.B.: Structure Optimization by an Improved Tabu Search in the AB Off-Lattice Protein Model. In: The 1st International Conference on Intelligent Networks and Intelligent Systems, pp. 123–126 (2008)Google Scholar
  12. 12.
    Lin, X.L., Yu, Z.H.: Effective Protein Folding Structure Prediction with the Local Adjustment Tabu Search Algorithm. In: ICMAI (2012)Google Scholar
  13. 13.
    Zhang, X.L., Lin, X.L.: Effective 3D Protein Structure Prediction With Local Adjustment Genetic-Annealing Algorithm. Interdiscip. Sci. Comput. Life Sci. 2, 1–7 (2010)CrossRefGoogle Scholar
  14. 14.
    Zhang, X.L., Lin, X.L., Wan, C.P., Li, T.T.: Genetic-annealing algorithm for 3D off-lattice protein folding model. The 2nd BioDM Workshop on Data Mining for Biomedical Applications, PAKDD Workshops, 186–193 (2007)Google Scholar
  15. 15.
    Turkensteen, M., Andersen, K.A.: A Tabu Search Approach to Clustering. In: Operations Research Proceedings (2008)Google Scholar
  16. 16.
    Tariq, R., Yi, W., Kuo-Bin, L.: Multiple Sequence Alignment Using Tabu Search. In: Proc. Second Asia-Pacific Bioinformatics Conference (2004)Google Scholar
  17. 17.
    Lecchini-Visintini, A., Lygeros, J., Maciejowski, J.: Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains. In: Advances in Neural Information Processing Systems 20, Proceedings of NIPS (2007)Google Scholar
  18. 18.
    Gatti, C.J., Hughes, R.E.: Optimization of Muscle Wrapping Objects Using Simulated Annealing. Annals of Biomedical Engineering 37, 1342–1347 (2009)CrossRefGoogle Scholar
  19. 19.
    Liang, F.: Annealing contour Monte Carlo algorithm for structure optimization in an off-lattice protein model. J. Chem. Phys. 120, 6756 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaoli Lin
    • 1
  • Fengli Zhou
    • 1
  1. 1.Information and Engineering Department of City CollegeWuhan University of Science and TechnologyWuhanChina

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