응용통계연구 (The Korean Journal of Applied Statistics)

  • 제22권3호
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  • Pages.627-634
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  • 2009
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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Real Protein Prediction in an Off-Lattice BLN Model via Annealing Contour Monte Carlo

  • Cheon, Soo-Young (KU Industry-Academy Cooperation Group Team of Economics and Statistics, Korea University)
  • 발행 : 2009.06.30
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초록

Recently, the general contour Monte Carlo has been proposed by Liang (2004) as a space annealing version(ACMC) for optimization problems. The algorithm can be applied successfully to determine the ground configurations for the prediction of protein folding. In this approach, we use the distances between the consecutive $C_{\alpha}$ atoms along the peptide chain and the mapping sequences between the 20-letter amino acids and a coarse-grained three-letter code. The algorithm was tested on the real proteins. The comparison showed that the algorithm made a significant improvement over the simulated annealing(SA) and the Metropolis Monte Carlo method in determining the ground configurations.

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참고문헌

  1. Gelman, A., Roberts, G. O. and Gilks, W. R. (1996). Efficient Metropolis jumping rules, In bayesian statistics 5, edited by J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith, 599-607. Oxford University Press
  2. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Professional, Michigan
  3. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57, 97-109 https://doi.org/10.1093/biomet/57.1.97
  4. Hesselbo, B. and Stinchcombe, R. B. (1995). Monte Carlo simulation and global optimization without parameters, Physical Review Letters, 74, 2151-2155 https://doi.org/10.1103/PhysRevLett.74.2151
  5. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems, The University of Michigan Press, Michigan
  6. Kim, S. Y., Lee, S. J. and Lee, J. (2003). Conformational space annealing and an off-lattice frustrated model protein. Journal of Chemical Physics, 119, 10274-10279 https://doi.org/10.1063/1.1616917
  7. Kim, S. Y., Lee, S. B. and Lee, J. (2005). Structure optimization by conformational space annealing in an off-lattice protein model, Physical Review E, 72, 011916-1-011916-6 https://doi.org/10.1103/PhysRevE.72.011916
  8. Kirkpatrick, S., Gelatt, Jr., C. D. and Vecchi, M. P. (1983). Optimization by simulated annealing, Science, 220, 671-680 https://doi.org/10.1126/science.220.4598.671
  9. Li, Z. and Scheraga, H. (1987). Monte Carlo-minimization approach to the multiple-minima problem in protein folding, Proceedings of the National Academy of Sciences of the United States of America, 84, 6611-6615 https://doi.org/10.1073/pnas.84.19.6611
  10. Liang, F. (2004). Annealing contour Monte Carlo algorithm for structure optimization in an off-lattice protein model, Journal of Chemical Physics, 120, 6756-6763 https://doi.org/10.1063/1.1665529
  11. Liang, F. (2005). A generalized Wang-Landau algorithm for Monte Carlo computation, Journal of the American Statistical Association, 100, 1311-1327 https://doi.org/10.1198/016214505000000259
  12. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. and Teller, E. (1953). Equation of state calculations by fast computing machines, Journal of Chemical Physics, 21, 1087-1092 https://doi.org/10.1063/1.1699114
  13. Wang, F. and Landau, D. P. (2001). Efficient multiple-range random walk algorithm to calculate the density of states, Physical Review Letters, 86, 2050-2053 https://doi.org/10.1103/PhysRevLett.86.2050
  14. Zhang, X., Lin, X., Wan, C. and Li, T. (2007). Genetic-annealing algorithm for 3D off-lattice protein folding model, Lecture Notes in Computer Science, 4819, 186-193 https://doi.org/10.1007/978-3-540-77018-3_20