Advertisement

PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design

  • Oliver Korb
  • Thomas Stützle
  • Thomas E. Exner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)

Abstract

A central part of the rational drug development process is the prediction of the complex structure of a small ligand with a protein, the so-called protein-ligand docking problem, used in virtual screening of large databases and lead optimization. In the work presented here, we introduce a new docking algorithm called PLANTS (Protein-Ligand ANTSystem), which is based on ant colony optimization. An artificial ant colony is employed to find a minimum energy conformation of the ligand in the protein’s binding site. We present the effectiveness of PLANTS for several parameter settings as well as a direct comparison to a state-of-the-art program called GOLD, which is based on a genetic algorithm. Last but not least, results for a virtual screening on the protein target factor Xa are presented.

Keywords

Root Mean Square Deviation Virtual Screening Rotatable Bond Heuristic Information Average Success Rate 
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.
    Müller, G.: Medicinal chemistry of target family-directed masterkeys. Drug Discovery Today 8(15), 681–691 (2003)CrossRefGoogle Scholar
  2. 2.
    Oprea, T., Matter, H.: Integrating virtual screening in lead discovery. Current Opinion in Chemical Biology 8, 349–358 (2004)CrossRefGoogle Scholar
  3. 3.
    Fischer, E.: Einfluss der Configuration auf die Wirkung der Enzyme. Chemische Berichte 27, 2985–2993 (1894)Google Scholar
  4. 4.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge, MA, USA (2004)MATHCrossRefGoogle Scholar
  5. 5.
    Kellenberger, E., Rodrigo, J., Muller, P., Rognan, D.: Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 57(2), 225–242 (2004)CrossRefGoogle Scholar
  6. 6.
    Kontoyianni, M., McClellan, L., Sokol, G.: Evaluation of docking performance: Comparative data on docking algorithms. Journal of Medicinal Chemistry 47(3), 558–565 (2004)CrossRefGoogle Scholar
  7. 7.
    Taylor, R., Jewsbury, P., Essex, J.: A review of protein-small molecule docking methods. Journal of Computer-Aided Molecular Design 16, 151–166 (2002)CrossRefGoogle Scholar
  8. 8.
    Stützle, T., Hoos, H.H.: \(\cal MAX\)\(\cal MIN\) Ant System. Future Generation Computer Systems 16(8), 889–914 (2000)CrossRefGoogle Scholar
  9. 9.
    Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer-Journal 7, 308–313 (1965)MATHGoogle Scholar
  10. 10.
    Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge (1992)Google Scholar
  11. 11.
    Gehlhaar, D.K., Verkhivker, G.M., Rejto, P.A., Sherman, C.J., Fogel, D.B., Fogel, L.J., Freer, S.T.: Molecular recognition of the inhibitor AG-1243 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chemistry and Biology 2, 317–324 (1995)CrossRefGoogle Scholar
  12. 12.
    Verdonk, M.L., Cole, J.C., Hartshorn, M.J., Murray, C.W., Taylor, R.D.: Improved protein-ligand docking using GOLD. Proteins 52, 609–623 (2003)CrossRefGoogle Scholar
  13. 13.
    Clark, M., Cramer III, R., van Opdenhosch, N.: Validation of the General Purpose Tripos 5.2 Force Field. Journal of Computational Chemistry 10, 982–1012 (1989)CrossRefGoogle Scholar
  14. 14.
    Nissink, J., Murray, C., Hartshorn, M., Verdonk, M., Cole, J., Taylor, R.: A new test set for validating predictions of protein-ligand interaction. Proteins 49(4), 457–471 (2002)CrossRefGoogle Scholar
  15. 15.
    Jones, G., Willett, P., Glen, R.C., Leach, A.R., Taylor, R.: Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology 267, 727–748 (1997)CrossRefGoogle Scholar
  16. 16.
    Jacobsson, M., Liden, P., Stjernschantz, E., Boström, H., Norinder, U.: Improving struture-based virtual screening by multivariate analysis of scoring data. Journal of Medicinal Chemistry 46(26), 5781–5789 (2003)CrossRefGoogle Scholar
  17. 17.
    Irwin, J., Shoichet, B.: ZINC - A Free Database of Commercially Available Compounds for Virtual Screening. Journal of Chemical Information and Modeling 45(1), 177–182 (2005)CrossRefGoogle Scholar
  18. 18.
    Halgren, T.: Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. Journal of Computational Chemistry 17(5-6), 490–519 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Oliver Korb
    • 1
  • Thomas Stützle
    • 2
  • Thomas E. Exner
    • 1
  1. 1.Theoretische Chemische DynamikUniversität KonstanzKonstanzGermany
  2. 2.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium

Personalised recommendations