Advertisement

Genetic Algorithms and Protein Folding

  • Steffen Schulze-Kremer
Part of the Methods in Molecular Biology™ book series (MIMB, volume 143)

Abstract

Genetic algorithms are, like neural networks, an example par excellence of an information-processing paradigm that was originally developed and exhibited by nature and later discovered by humans, who subsequently transformed the general principle into computational algorithms to be put to work in computers. Nature uses the principle of genetic heritage and evolution in an impressive way. Application of the simple concept of performance based reproduction of individuals (“survival of the fittest”) led to the rise of well-adapted organisms that can endure in a potentially adverse environment. Mutually beneficial interdependencies, cooperation, and even apparently altruistic behavior can emerge solely by evolution. The investigation of those phenomena is part of research in artificial life but is not dealt with here.

Keywords

Genetic Algorithm Fitness Function Torsion Angle Genetic Operator Native Conformation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Holland, J. H. (1973) Genetic algorithms and the optimal allocations of trials. SIAM J. Comput. 2, 88–105.CrossRefGoogle Scholar
  2. 2.
    Rechenberg, I. (1973) Bioinik, Evolution und Optimierung. Naturwissenschaftliche Rundschau 26, 465–472.Google Scholar
  3. 3.
    Koza, J. (1993) Genetic Programming, MIT Press.Google Scholar
  4. 4.
    Kirkpatrick, S., Gelatt, C. D., Jr., and Vecchi, M. P. (1983) Optimization by simulated annealing. Science 4598, 671–680.CrossRefGoogle Scholar
  5. 5.
    Jones, T. and Forrest, S. (1993) An Introduction to SFI Echo, Santa Fe Institute, Santa Fe, NM. E-mail: terry@sanatfe.edu, forrest@cs.unm.edu. World Wide Web Server at ftp://alife. santafe. edu/pub/SOFTWARE.Google Scholar
  6. 6.
    Holland, J. H. (1993) Echoing emergence: objectives, rough definitions and speculations for echo-class models, in Integrative Themes (Cowan, G., Pines, D., and Melzner, D., eds.), Santa Fe Institute Studies in the Science of Complexity, Proc. Vol XIX, Addison-Wesley, Reading, MA.Google Scholar
  7. 7.
    Holland, J. H. (1992) Adaptation in Natural and Artificial Systems. 2nd Ed., MIT Press.Google Scholar
  8. 8.
    Davis, L. (ed.) (1991) Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York.Google Scholar
  9. 9.
    Goldberg, D. E. (1989) Genetic algorithms, in Search, Optimization & Machine Learning. Addison-Wesley.Google Scholar
  10. 10.
    Unger, R. and Moult, J. (1993) Genetic algorithms for protein folding simulations. J. Mol. Biol. 231, 75–81.PubMedCrossRefGoogle Scholar
  11. 12.
    Schulz, G. E. and Schirmer, R. H. (1979) Principles of Protein Structure. Springer Verlag, New York.Google Scholar
  12. 13.
    Lesk, A. M. (1991) Protein Architecture — A Practical Approach. IRL Press at Oxford University Press, Oxford, UK.Google Scholar
  13. 14.
    Branden, C. and Tooze, J. (1991) Introduction to Protein Structure. Garland Publishing, New York.Google Scholar
  14. 15.
    Schulze-Kremer, S. (1992) Genetic algorithms for protein tertiary structure prediction, in Parallel Problem Solving from Nature II (Männer, R. and Manderick, B., eds.), North Holland, Amsterdam, pp. 391–400.Google Scholar
  15. 16.
    Dandekar, T. and Argos, P. (1992) Potential of genetic algorithms in protein folding and protein engineering simulations. Protein Eng. 7, 637–645.CrossRefGoogle Scholar
  16. 17.
    Le Grand, S. M. and Merz, K. M. (1993) The application of the genetic algorithm to the minimization of potential energy functions. J. Global Opt. 3, 49–66.CrossRefGoogle Scholar
  17. 18.
    Sun, S. (1994) Reduced representation model of protein structure prediction: statistical potential and genetic algorithms. Prot. Sci. 5, 762–785.Google Scholar
  18. 19.
    Dandekar, T. and Argos, P. (1994) Folding the main chain of small proteins with the genetic algorithm. J. Mol. Biol. 236, 844–861.PubMedCrossRefGoogle Scholar
  19. 20.
    Bernstein, F. C., Koetzle, T. F., Williams, G. J. B., Meyer, E. F., Jr., Brice, M. D., Rodgers, J. R., Kennard, O., Shimanouchi, T., and Tasumi, M. (1997) The protein data bank: a computer-based archival file for macromolecular structures. J. Mol. Biol. 112, 535–542.CrossRefGoogle Scholar
  20. 21.
    Vinter, J. G., Davis, A., and Saunders, M. R. (1987) Strategic approaches to drug design. An integrated software framework for molecular modelling. J. Comput. Aided Mol. Des. 1, 31–51.PubMedCrossRefGoogle Scholar
  21. 22.
    Brooks, B. R., Bruccoleri, R. E., Olafson, B. D., States, D. J., Swaminathan, S., and Karplus, M. (1983) Charmm: a program for macromolecular energy, minimization and dynamics calculations. J. Comput. Chem. 4, 187–217.CrossRefGoogle Scholar
  22. 23.
    Ngo, J. T. and Marks, J. (1992) Computational complexity of a problem in molecular-structure prediction. Protein Eng. 5, 313–321.PubMedCrossRefGoogle Scholar
  23. 24.
    Davis, L. (ed.) (1991) Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York.Google Scholar
  24. 25.
    Lucasius, C. B. and Kateman, G. (1989) Application of genetic algorithms to chemometrics, in Proceedings of the 3rd International Conference on Genetic Algorithms (Schaffer, J. D., ed.), Morgan Kaufmann Publishers, San Mateo, CA, pp. 170–176.Google Scholar
  25. 26.
    Tuffery, P., Etchebest, C., Hazout, S., and Lavery, R. (1991) A new approach to the rapid determination of protein side chain conformations. J. Biomol. Struct. Dyn. 8, 1267–1289.PubMedGoogle Scholar
  26. 27.
    Hendrickson, W. A. and Teeter, M M. (1981) Structure of the hydrophobic protein crambin determined directly from the anomalous scattering of sulphur. Nature 290, 107.CrossRefGoogle Scholar
  27. 28.
    Lee, C. and Subbiah, S. (1991) Prediction of protein side chain conformation by packing optimization. J. Mol. Biol. 217, 373–388.PubMedCrossRefGoogle Scholar
  28. 29.
    Tuffery, P., Etchebest, C., Hazout, S., and Lavery, R. (1991) A new approach to the rapid determination of protein side chain conformations. J. Biomol. Struct. Dyn. 8, 1267–1289.PubMedGoogle Scholar
  29. 30.
    Maiorov, N. M. and Crippen, G. M. (1992) Contact potential that recognizes the correct folding of globular proteins. J. Mol. Biol. 227, 876–888.PubMedCrossRefGoogle Scholar
  30. 31.
    Go, N. and Scheraga, H. A. (1970) Ring closure and local conformational deformations of chain molecules. Macromolecules 3, 178–187.CrossRefGoogle Scholar
  31. 32.
    Lu, S. Y. and Fu, K. S. (1978) A sentence-to-sentence clustering procedure for pattern analysis. IEEE Trans. Syst. Man Cybern. 8, 381–389.CrossRefGoogle Scholar
  32. 33.
    Rost, B. and Sander, C. (1993) Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol. 232, 584–599.PubMedCrossRefGoogle Scholar
  33. 34.
    Cármenes, R. S., Freije, J. P., Molina, M. M., and Martín, J. M. (1989) PREDICT7, a program for protein structure prediction. Biochem. Biophys. Res. Commun. 159, 687–693.PubMedCrossRefGoogle Scholar
  34. 35.
    Garnier, J., Osguthorpe, D. J., and Robson, B. (1978) Analysis of the accuracy and implications of simple methods for predicting the secondary structure of global proteins. J. Mol. Biol., 120, 97–120.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc. 2000

Authors and Affiliations

  • Steffen Schulze-Kremer
    • 1
  1. 1.Max-Planck Institute for Molecular GeneticsBerlinGermany

Personalised recommendations