Advertisement

FReDD: Supporting Mining Strategies through a Flexible-Receptor Docking Database

  • Ana T. Winck
  • Karina S. Machado
  • Osmar Norberto-de-Souza
  • Duncan D. D. Ruiz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5676)

Abstract

Among different alternatives to consider the receptor flexibility in molecular docking experiments we opt to execute a series of docking using receptor snapshots generated by molecular dynamics simulations. Our target is the InhA enzyme from Mycobacterium tuberculosis bound to NADH, TCL, PIF and ETH ligands. After testing some mining strategies on these data, we conclude that, to obtain better outcomes, the development of an organized repository is especially useful. Thus, we built a comprehensive and robust database called FReDD to store the InhA-ligand docking results. Using this database we concentrate efforts on data mining to explore the docking results in order to accelerate the identification of promising ligands against the InhA target.

Keywords

Molecular docking database data preparation data mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Drews, J.: Drug discovery: A historical perspective computational methods for biomolecular docking. Current Opinion in Structural Biology 6, 402–406 (1996)CrossRefGoogle Scholar
  2. 2.
    Kuntz, I.D.: Structure-based strategies for drug design and discovery. Science 257, 1078–1082 (1992)CrossRefPubMedGoogle Scholar
  3. 3.
    Lybrand, T.P.: Ligand-protein docking and rational drug design. Current Opinion in Structural Biology 5, 224–228 (1995)CrossRefPubMedGoogle Scholar
  4. 4.
    Totrov, M., Abagyan, R.: Flexible ligand docking to multiple receptor conformations: a pratical alternative. Current Opinion in Structural Biology 18, 178–184 (2008)CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Cozzini, P., et al.: Target Flexibility: An Emerging Consideration in Drug Discovery and Design. Journal of Medicinal Chemistry 51, 6237–6255 (2008)CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Alonso, H., et al.: Combining Docking and Molecular Dynamic Simulations in Drug Design. Med. Res. Rev. 26, 531–568 (2006)CrossRefPubMedGoogle Scholar
  7. 7.
    van Gunsteren, W.F., Berendsen, H.J.C.: Computer Simulation of Molecular Dynamics Methodology, Applications and Perspectives in Chemistry. Angew. Chem. Int. Ed. Engl. 29, 992–1023 (1990)CrossRefGoogle Scholar
  8. 8.
    Dessen, A., et al.: Crystal structure and function of the isoniazid target of Mycobacterium tuberculosis. Science 267, 1638–1641 (1995)CrossRefPubMedGoogle Scholar
  9. 9.
    Pearlman, D.A., et al.: AMBER, a computer program for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to elucidate the structures and energies of molecules. Comp. Phys. Commun. 91, 1–41 (1995)CrossRefGoogle Scholar
  10. 10.
    Schroeder, E.K., et al.: Molecular Dynamics Simulation Studies of the Wild-Type, I21V, and I16T Mutants of Isoniazid-Resistant Mycobacterium tuberculosis Enoyl Reductase (InhA) in Complex with NADH: Toward the Understanding of NADH-InhA Different Affinities. Biophysical Journal 89, 876–884 (2005)CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Kuo, M.R., et al.: Targeting tuberculosis and malaria through inhibition of enoyl reductase: compound activity and structural data. J. Biol. Chem. 278, 20851–20859 (2003)CrossRefPubMedGoogle Scholar
  12. 12.
    Oliveira, J.S., et al.: An inorganic iron complex that inhibits wild-type and an isoniazid-resistant mutant 2-trans-enoyl-ACP (CoA) reductase from Mycobacterium tuberculosis. Chem. Comm. 3, 312–313 (2004)CrossRefGoogle Scholar
  13. 13.
    Machado, K.S., et al.: Automating Molecular Docking with Explicit Receptor Flexibility Using Scientific Workflows. In: Sagot, M.-F., Walter, M.E.M.T. (eds.) BSB 2007. LNCS (LNBI), vol. 4643, pp. 1–11. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Morris, G.M., et al.: Automated Docking Using a Lamarckian Genetic Algorithm and Empirical Binding Free Energy Function. J. Comput. Chemistry 19, 1639–1662 (1998)CrossRefGoogle Scholar
  15. 15.
    Machado, K.S., et al.: Extracting Information from Flexible Receptor-Flexible Ligand Docking Experiments. In: Bazzan, A.L.C., Craven, M., Martins, N.F. (eds.) BSB 2008. LNCS (LNBI), vol. 5167, pp. 104–114. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Waikato Environment for Knowledge, Analysis, http://www.cs.waikato.ac.nz/ml/weka

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ana T. Winck
    • 1
  • Karina S. Machado
    • 1
  • Osmar Norberto-de-Souza
    • 1
  • Duncan D. D. Ruiz
    • 1
  1. 1.PPGCC, Faculdade de Informática, PUCRSPorto AlegreBrazil

Personalised recommendations