DOI QR코드

DOI QR Code

Comparison of Recombination Methods ad Cooling Factors in Genetic Algorithms Applied to Folding of Protein Model System


초록

We varied recombination method of fenetic algorithm (GA), i.e., crossover step, to compare efficiency of these methods, and to find more optimum GA method. In one method (A), we select two conformations(parents) to be recombined by systematic combination of lowest energy conformations, and in the other (B), we select them in a ratio proportional to the energy of the conformation. Second variation lies in how to select crossover point. First, we select it randomly(1). Second, we select range of residues where internal energy of the molecule does not vary for more than two residues, select randomly among such regions, and we select either thr first (2a) or the second residue (2b) from the N-terminal side, or the first (2c) or the second residue (2d) from the C-terminal side in the selected region for crossover point. Third, we select longest such hregion, and select such residue(as cases 2) (3a, 3b, 3c or 3d) of the region. These methods were tested in a 2-dimensionl lattice system for 8 different sequences (the same ones used by Unger and Moult., 1993). Results show that compared to Unger and Moult's result(UM) which corresponds to B-1 case, our B-1 case performed similarly in overall. There are many cases where our new methods performed better than UM for some different sequences. When cooling factor affecting higher energy conformation to be accepted in Monte Carlo step was reduced, our B-1 and other cases performed better than UM; we found lower energy conformers, and found same energy conformers in a smaller steps. We discuss importance of cooling factor variation in Monte Carlo simulations of protein folding for different proteins. (A) method tends to find the minimum conformer faster than (B) method, and (3) method is superior or at least equal to (1) method.

키워드

참고문헌

  1. Adaptation in Natural and Artificial Systems Holland, J. H.
  2. Scientific American v.44 Holland, J. H.
  3. Science v.261 no.872 Forrest, S.
  4. Genetic Algorithms in Search, Optimization, and Machine Learning Goldberg, D. E.
  5. Genetic Algorithms and Robotics: A Heuristic Strategy of Optimization Davidor, T.
  6. Parallel Problem Solving from Nature 2 Schulze-Kremer, S.;Manner, R.(ed.)Manderick, B.(ed.)
  7. J. Mol. Biol. v.231 no.75 Unger, R.;Moult, J.
  8. Protein Science v.2 no.762 Sun, S.
  9. J. Mol. Biol. v.236 no.844 Dandekar, T.;Argos, P.
  10. Proteins: Struct., Funct. Genetics v.23 no.454 Perdersen, J. T.;Moult, J.
  11. J. theor. Biol. v.174 no.269 Van Batenburg, F. H. D.;Gultyaev, A. P.;Pleij, C. W.
  12. J. Comput-Aided Mol. Design v.9 no.113 Oshiro. C. M.;Kuntz, I. D.;Dixon, J. S.
  13. J. Chim. Phys. v.65 no.44 Levinthal, C.
  14. J. Mol. Biol. v.221 no.961 Rooman, M.;Kocher, J.-P.;Wodak, S.
  15. J. Mol. Biol. v.229 no.448 Kang, H. S.;Kurochkina, N. A.;Lee. B.
  16. Proteins: Struct., Funct. Genetics v.23 no.73 Elofsson, A.;Le Grand, S. M.;Eisenberg, D.
  17. Prot. Engineering v.8 no.769 Sun, S.;Thomas, P. D.;Dill, K. A.
  18. J. Mol. Biol. v.269 no.240 Perdersen, J. T.;Moult, J.
  19. Protein Engineering v.10 no.877 Dandekar, T.;Argos, P.
  20. Protein Science v.7 no.491 Bailey, M. J.;Jones, G.;Willett, P.;Williamson, M.
  21. J. Phys. Chem. v.95 no.4141 Samorjai, R. L.
  22. J. Phys. Chem. v.93 no.3339 Piela, L.;Kostrowicki, J.;Scheraga, H. A.
  23. Proteins: Struct., Funct. Genetics v.22 no.81 Srinivasan, R.;Rose, G. D.
  24. J. Chem. Phys. v.21 no.1087 Metropolis, N.;Rosenbluth, A. W.;Rosenbluth, M. N.;Teller, A. H.;Teller, E.
  25. Biochemistry v.29 no.7133 Dill, K. A.
  26. Biochemistry v.29 no.3287 Covell, D. G.;Jernigan, R. L.
  27. Science v.250 no.1121 Skolnick, J.;Kolinsky, A.
  28. Phys. Rev. Let. v.67 no.1665 Shakhnovich, E.;Fardinov, G.;Gutin, A. M.;Karplus, M.