Advertisement

Side-Chain Structure Prediction Based on Dead-End Elimination: Single Split DEE-criterion Implementation and Elimination Power

  • Jan A. Spriet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2812)

Abstract

The three-dimensional structure of a protein is a very fundamental piece of knowledge. Part of the problem of obtaining that knowledge is modeling the side-chain spatial positions. Dead-End Elimination (DEE) is a powerful philosophy concerning search in the abstract conformational space, meant to yield the optimal side-chain orientations, fixed on the given protein backbone. The approach permits the formulation of several DEE-criteria, which differ in quality and computational efficiency for the abstract optimization problem that is rooted in and framed by the macromolecular architectural aspects of the protein universe. The present work investigates time complexity, elimination power and optimized implementation of the recently proposed Single Split DEE- criterion for protein side-chain placement or prediction. Properly inserted in a suitable DEE-cycle, the criterion is found to follow the classic cost bound and is proven to be worthwhile. On a test set of sixty proteins, the elimination power is 17.7%, in addition to the combined pruning effect of the standard criteria. It is also found that algorithm implementation can be optimized using the efficacy – ”Magic Bullet Character”, of the side-chain residue types.

Keywords

Packing Problem Conformational Space Integer Programming Problem Elimination Capacity Amino Acid Type 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Vasquez, M.: Modeling side-chain conformation. Curr. Opin. Struct. Biol. 6, 217–221 (1996)CrossRefGoogle Scholar
  2. 2.
    Desmet, J., De Maeyer, M., Lasters, I.: Theoretical and algorithmical optimization of the dead-end elimination theorem. In: Altman, R., et al. (eds.) Proceedings of the Pacific Symposium on Biocomputing 1997, pp. 122–133. World Scientific, New Jersey (1997)Google Scholar
  3. 3.
    Pierce, N., Spriet, J., Desmet, J., Mayo, S.: Conformational Splitting: A More Powerful Criterion for Dead-End Elimination. J. Comput. Chem. 11, 999–1009 (2000)CrossRefGoogle Scholar
  4. 4.
    Janin, J., Wodak, S., Levitt, M., Maigret, D.: Conformation of amino acid sidechains in proteins. J. Mol. Biol. 125, 357–386 (1978)CrossRefGoogle Scholar
  5. 5.
    Ponder, P., Richards, F.: Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequences for different structural classes. J. Mol. Biol. 193, 775–791 (1987)CrossRefGoogle Scholar
  6. 6.
    Dunbrack, R., Karplus, M.: Backbone-dependent rotamer library for proteins: application to side-chain prediction. J. Mol. Biol. 230, 543–574 (1993)CrossRefGoogle Scholar
  7. 7.
    Eriksson, O., Zhou, Y., Elofsson, A.: Side-Chain Positioning as an Integer Programming Problem. In: Gascuel, O., Moret, B.M.E. (eds.) WABI 2001. LNCS, vol. 2149, pp. 128–141. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Desmet, J., De Maeyer, M., Hazes, B., Lasters, I.: The Dead-End Elimination Theorem and its Use in Protein Side-Chain Positioning. Nature 356, 539–542 (1992)CrossRefGoogle Scholar
  9. 9.
    Lee, E., Subbiah, S.: Prediction of Protein Side-chain Conformation by Packing Optimization. J. Mol. Biol. 217, 373–388 (1991)CrossRefGoogle Scholar
  10. 10.
    Eisenmenger, F., Argos, P., Abagyan, R.: A Method to Configure Protein Sidechains from the Main-chain Trace in Homology Modelling. J. Mol. Biol. 231, 849–860 (1993)CrossRefGoogle Scholar
  11. 11.
    Xiang, Z., Honig, B.: Extending the Accuracy Limits of Prediction for Side-chain Conformations. J. Mol. Biol. 311, 421–430 (2001)CrossRefGoogle Scholar
  12. 12.
    Goldstein, R.: Efficient rotamer elimination applied to protein side-chains and related spin glasses. Biophysic. Journ. 66, 1335–1340 (1994)CrossRefGoogle Scholar
  13. 13.
    Fraenkel, A.: Protein folding, spin glass and computational complexity. In: Third annual DIMACS workshop on DNA based computers. Proceedings, Philadelphia, pp. 23–25 (1997)Google Scholar
  14. 14.
    Desmet, J., De Maeyer, M., Lasters, I.: The Dead-End Elimination Theorem: a New Approach to the Side-Chain Packing Problem. In: Merz, K., Le Grand, S. (eds.) The Protein Folding Problem and Tertiary Structure Prediction, Birkhuser, Boston, pp. 307–337 (1994)Google Scholar
  15. 15.
    Dahiyat, B., Mayo, S.: De novo protein design: fully automated sequence selection. Science 278, 82–87 (1997)CrossRefGoogle Scholar
  16. 16.
    Lasters, I., De Mayer, M., Desmet, J.: Enhanced dead-end elimination in the search for the global minimum energy conformation of a collection of protein side-chains. Prot. Eng. 8, 815–822 (1995)CrossRefGoogle Scholar
  17. 17.
    Desmet, J., Spriet, J., Lasters, I.: Fast and Accurate Side-Chain Topology and Energy Refinement (FASTER) as a New Method for Protein Structure Optimization. PROTEINS: Struct. Funct. Genet. 48, 31–34 (2002)CrossRefGoogle Scholar
  18. 18.
    Hobohm, U., Scharf, M., Schneider, R., Sander, C.: Selection of representative protein data sets. Prot. Sci. 1, 409–417 (1992)CrossRefGoogle Scholar
  19. 19.
    Hobohm, U., Sander, C.: Enlarged representative set of protein structures. Prot. Sci. 3, 522–524 (1994)CrossRefGoogle Scholar
  20. 20.
    Bernstein, F., Koetzle, T., Williams, G., Meyer Jr, E., Brice, M., Rodgers, J., Kennard, O., Shimanouchi, T., Tasumi, M.: The Protein Data Dank: a computerbased archival file for macromolecular structures. J. Mol. Biol. 112, 535–542 (1977)CrossRefGoogle Scholar
  21. 21.
    Brooks, B., Bruccoleri, R., Olafson, D., States, D., Swaminathan, S., Karplus, M.: CHARMM: a program for macromolecular energy minimization and dynamics calculations. J. Comput. Chem. 4, 187–217 (1983)CrossRefGoogle Scholar
  22. 22.
    De Maeyer, M., Desmet, J., Lasters, I.: All in One: a Highly Detailed Rotamer Library Improves both Accuracy and Speed in the Modelling of Sidechains by Dead-End Elimination. Folding Design 2, 53–66 (1997)CrossRefGoogle Scholar
  23. 23.
    Delhaise, P., Bardiaux, M., Wodak, S.: Interactive computer animation of macromolecules. J. Mol. Graph. 2, 103–106 (1984)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jan A. Spriet
    • 1
  1. 1.Katholieke UniversiteitKortrijkBelgium

Personalised recommendations