Volume 3 Supplement 1

6th German Conference on Chemoinformatics, GCC 2010

Open Access

Virtual screening using structure-based consensus pharmacophore models and ensemble docking based on MD-generated conformations

  • Oliver Koch1, 2Email author,
  • Daniel Cappel3, 4,
  • Monika Nocker3,
  • Timo Jaeger2,
  • Leopold Flohé2,
  • Christoph Sotriffer3 and
  • Paul Selzer1
Journal of Cheminformatics20113(Suppl 1):O23

DOI: 10.1186/1758-2946-3-S1-O23

Published: 19 April 2011

The protozoan parasites of the genus Trypanosoma sp. and Leishmania sp. are responsible for neglected diseases like Chagas’ disease, African sleeping sickness or Leishmaniasis. The trypanothione synthetase (TryS) is an attractive new drug target for the development of trypanocidal and antileishmanial drugs [1].

In our virtual screening campaign for targeting the trypanothione synthetase (TryS) we used representative protein conformations derived from a computational analysis using molecular dynamics (MD) simulations of this key component of trypanothione biosynthesis. The publicly available crystal structure lacks a variable loop region that is known to be important for trypanothione biosynthesis. MD simulations turned out to be a good tool to model this loop region and obtain a more complete set of protein conformations for subsequent use in virtual screening [2].

For creating a structure-based consensus pharmacophore model, Superstar [3] was deployed to generate favourable non-bonded interaction maps of different functional groups (probes) for all representative protein conformations. The pharmacophore model was then created for the rigid part of the binding pocket based on high- propensity peaks of these maps. The variable loop region was left out since it can not be depicted by this approach. To include also multiple conformations of the variable loop region, the new ensemble docking feature of Gold was used [4]. After a pharmacophore search within the ZINC database the retrieved molecules were simultaneously docked to the different protein conformations to identify the best combination of ligand pose and protein conformer. Finally, several high-scoring molecules were selected for further testing.

We will discuss in detail this combined pharmacophore/ensemble docking approach based on MD simulations and will present the results of the compound selection for testing.

Authors’ Affiliations

(1)
Intervet Innovation GmbH, Zur Propstei
(2)
MOLISA GmbH
(3)
Institute of Pharmacy and Food Chemistry, Am Hubland
(4)
Schrödinger GmbH

References

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  3. Verdonk ML, Cole JC, Watson P, Gillet V, Willett P: SuperStar: Improved knowledge-based interaction fields for protein binding sites. Journal of Molecular Biology. 2001, 307: 841-859. 10.1006/jmbi.2001.4452.View ArticleGoogle Scholar
  4. Korb O, Bowden S, Olsson T, Frenkel D, Liebeschuetz J, Cole JC: Ensemble docking revisited. Journal of Chemoinformatics. 2010, 2 (1): P25-10.1186/1758-2946-2-S1-P25.View ArticleGoogle Scholar

Copyright

© Koch et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.