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Analytically Tuned Simulated Annealing Applied to the Protein Folding Problem

  • Juan Frausto-Solis
  • E. F. Román
  • David Romero
  • Xavier Soberon
  • Ernesto Liñán-García
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4488)

Abstract

In this paper a Simulated Annealing algorithm (SA) for solving the Protein Folding Problem (PFP) is presented. This algorithm has two phases: quenching and annealing. The first phase is applied at very high temperatures and the annealing phase is applied at high and low temperatures. The temperature during the quenching phase is decreased by an exponential function. We run through an efficient analytical method to tune the algorithm parameters. This method allows the change of the temperature in accordance with solution quality, which can save large amounts of execution time for PFP.

Keywords

Peptide Protein Folding Simulated Annealing 

References

  1. 1.
    Anfinsen, C.: Principles that govern the folding of protein chains. Science 181, 223–230 (1973)CrossRefGoogle Scholar
  2. 2.
    Levinthal, C.: Are there pathways for protein folding? J. Chem. Phys. 65, 44–45 (1968)Google Scholar
  3. 3.
    Ponder, J.: Case, Force fields for protein simulations. Adv. Prot. Chem. 66, 27–85 (2003)CrossRefGoogle Scholar
  4. 4.
    Brooks, R., Bruccoleri, R., Olafson, B., States, D., Swaminathan, S., Karplus, M.: A program for macromolecular energy, minimization, and dynamics calculations. J. Comp. Chem. 4, 187–217 (1983)CrossRefGoogle Scholar
  5. 5.
    Momany, F., McGuire, R., Burgess, A., Scheraga, H.: Energy Parameters in Polypeptide. VII. Geometric Parameters, Partial Atomic Charges, Nonbonded Interactions, Hydrogen Bond Interactions, and Intrinsic Torsional Potentials for the Naturally Occurring Amino Acids. The Journal of Physical Chemistry 79(22) (1975)Google Scholar
  6. 6.
    Nemethy, G., Gibson, K., Palmer, K., Yoon, C., Paterlini, G., Zagari, A., Rumsey, S., Scheraga, H.: Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm with application to proline-containing peptides. J. Phys. Chem. 18, 323 (1992)Google Scholar
  7. 7.
    Morales, L., Garduño, R., Romero, D.: Application for simulated annealing to the multiple - minima problem in small peptides. J. Biomol. Str. And Dyn. 8, 1721–1735 (1991)Google Scholar
  8. 8.
    Morales, L., Garduño, R., Romero, D.: The multiple - minima problem in small peptide revisited. The threshold accepting approach. J. Biomol. Str. And Dyn. 9 (1992)Google Scholar
  9. 9.
    Hansmann, U., Okamoto, Y.: Prediction of Peptide Conformation by the Multicanonical Algorithm. arXiv: cond-mat/9303024 v1 (1993)Google Scholar
  10. 10.
    Okamoto, Y.: Protein Folding Problem as Studied by New Simulation Algorithms. Recent Research Developments in Pure & Applied Chemistry. Proc. Acad. Sci. USA 84, 6611–6615 (1998)Google Scholar
  11. 11.
    Garduño, R., Romero, D.: Heuristic Methods in conformational space search of peptides. J. Mol. Str. 308, 115–123 (1994)Google Scholar
  12. 12.
    Simons, K., Kooperberg, C., Huang, E., Baker, D.: Assembly of Protein Tertiary Structures from Fragments with Similar Local Sequences using Simulated Annealing and Bayesian Scoring Functions. J. Mol. Biol. 268, 209–225 (1997)CrossRefGoogle Scholar
  13. 13.
    Pillardy, J., Czaplewski, C., Liwo, A., Lee, J., Ripoll, D., Kazmierkiewicz, R., Odziej, S., Wedemeyer, W., Gibson, K., Arnautova, Y., Saunders, J., Ye, Y., Scheraga, H.: Recent improvements in prediction of protein structure by global optimization of a potential energy function. PNAS 98(5), 2329–2333 (2000)CrossRefGoogle Scholar
  14. 14.
    Hiroyasu, T., Miki, M., Ogura, S., Aoi, K., Yoshida, T., Okamoto, Y., Dongarra, J.: Energy Minimization of Protein Tertiary Structure by Parallel Simulated Annealing using Genetic Crossover. In: Proceedings of 2002 Genetic and Evolutionary Computation Conference (GECCO 2002) Workshop Program, pp. 49–51 (2002)Google Scholar
  15. 15.
    Vila, J., Ripoll, D., Scheraga, H.: Atomically detailed folding simulation of the B domain of staphylococcal protein A from random structures. PNAS 100(25), 14812–14816 (2003)CrossRefGoogle Scholar
  16. 16.
    Hung, L., Samudrala, R.: PROTINFO: Secondary and tertiary protein structure prediction. Nucleic Acids Research 31(13), 3296–3299 (2003)CrossRefGoogle Scholar
  17. 17.
    Chen, W., Li, K., Liu, J.: The simulated annealing method applied to protein structure prediction. In: Third international conference on machine learning and cybernetics, Shanghai (2004)Google Scholar
  18. 18.
    Liwo, A., Khalili, M., Scheraga, H.: Ab initio simulations of protein-folding pathways by molecular dynamics with the united-residue model of polypeptide chains. PNAS 102(7), 2362–2367 (2004)CrossRefGoogle Scholar
  19. 19.
    Alves, R., Degréve, L., Caliri, A.: LMProt: An Efficient Algorithm for Monte Carlo Sampling of Protein Conformational Space. Biophysical Journal; ProQuest Medical Library 87(3) (2004)Google Scholar
  20. 20.
    Lee, J., Kim, S., Lee, J.: Protein structure prediction based on fragment assembly and parameter optimization. Biophisycal Chemestry 115, 209–214 (2005)CrossRefGoogle Scholar
  21. 21.
    Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 4598(220), 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Cerny, V.: Thermo dynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications 45(1), 41–51 (1985)zbMATHCrossRefMathSciNetGoogle Scholar
  23. 23.
    Sanvicente-Sánchez, H., Frausto-Solís, J.: A method to establish the cooling scheme in simulated annealing like algorithms. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3045, pp. 755–763. Springer, Heidelberg (2004)Google Scholar
  24. 24.
    Sanvicente, H.: Metodología de paralelización del ciclo de temperatura en algoritmos tipo recocido simulado. Tesis doctoral, ITESM Campus Cuernavaca, México (2003)Google Scholar
  25. 25.
    Sanvicente, H., Frausto, J.: Optimización de los diámetros de las tuberías de una red de distribución de agua mediante algoritmos de recocido simulado. Ingeniería hidráulica en México XVIII(1), 105–118 (2003)Google Scholar
  26. 26.
    Sanvicente–Sánchez, H., Frausto–Solís, J., Imperial–Valenzuela, F.: Solving SAT Problems with TA Algorithms Using Constant and Dynamic Markov Chains Length. In: Megiddo, N., Xu, Y., Zhu, B. (eds.) AAIM 2005. LNCS, vol. 3521, pp. 281–290. Springer, Heidelberg (2005)Google Scholar
  27. 27.
    Frausto-Solís, J., Sanvicente-Sánchez, H., Imperial-Valenzuela, F.: ANDYMARK: An analytical method to establish dynamically the length of the markov chain in simulated annealing for the satisfiability problem. In: Wang, T.-D., Li, X.-D., Chen, S.-H., Wang, X., Abbass, H.A., Iba, H., Chen, G.-L., Yao, X. (eds.) SEAL 2006. LNCS, vol. 4247, pp. 269–276. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  28. 28.
    Eisenmenger, F., Hansmann, U., Hayryan, S., Hu, C.: SMMP: A modern Package for Protein Simulation. Comp. Phys. Comm. 138, 192 (2001)zbMATHCrossRefGoogle Scholar
  29. 29.
    Eisenmenger, F., Hansmann, U., Hayryan, S., Hu, C.: An Enhanced Version of SMMP - Open source software package for simulation of proteins. Comp. Phys. Comm., 174–422 (2006)Google Scholar
  30. 30.
    Ramachandran, G.N., Ramakrishnan, C., Sasisekharan, V.: Stereochemistry of polypeptide chain configuration. J. Mol. Biol. 7, 95–99 (1963)CrossRefGoogle Scholar
  31. 31.
    Pérez, J., Pazos, R.A., Velez, L., Rodríguez, G.: Automatic generation of control parameters for the threshold accepting algorithm. In: Coello Coello, C.A., de Albornoz, Á., Sucar, L.E., Battistutti, O.C. (eds.) MICAI 2002. LNCS (LNAI), vol. 2313, pp. 118–127. Springer, Heidelberg (2002)Google Scholar
  32. 32.
    Pérez, O.J., Pazos, R.A., Romero, D., Santaolaya, R., Rodríguez, G., Sosa, V.: Adaptive and Scalable Allocation of Data-Objects in the Web. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds.) ICCSA 2003. LNCS, vol. 2667, pp. 134–143. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Juan Frausto-Solis
    • 1
  • E. F. Román
    • 1
  • David Romero
    • 2
  • Xavier Soberon
    • 3
  • Ernesto Liñán-García
    • 4
  1. 1.ITESM Campus Cuernavaca, Paseo de la Reforma 182-A Col. Lomas de Cuernavaca, 62589, Temixco MorelosMéxico
  2. 2.IMASS UNAM 
  3. 3.IBT UNAM 
  4. 4.Universidad Autónoma de Coahuila 

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