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This article is part of the supplement: 4th German Conference on Chemoinformatics: 22. CIC-Workshop

Open Access Poster presentation

PocketGraph: graph representation of binding site volumes

M Weisel*, J Kriegl and G Schneider

  • * Corresponding author: M Weisel

Author Affiliations

Johann Wolfgang Goethe-University, Beilstein Endowed Chair for Cheminformatics, Siesmayerstraße 70, D-60323 Frankfurt/Main, Germany

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Chemistry Central Journal 2009, 3(Suppl 1):P66 doi:10.1186/1752-153X-3-S1-P66


The electronic version of this article is the complete one and can be found online at: http://www.journal.chemistrycentral.com/content/3/S1/P66


Published:5 June 2009

© 2009 Weisel et al; licensee BioMed Central Ltd.

Poster presentation

The representation of small molecules as molecular graphs [1] is a common technique in various fields of cheminformatics. This approach employs abstract descriptions of topology and properties for rapid analyses and comparison. Receptor-based methods in contrast mostly depend on more complex representations impeding simplified analysis and limiting the possibilities of property assignment. In this study we demonstrate that ligand-based methods can be applied to receptor-derived binding site analysis.

We introduce the new method PocketGraph that translates representations of binding site volumes into linear graphs and enables the application of graph-based methods to the world of protein pockets. The method uses the PocketPicker [2] algorithm for characterization of binding site volumes and employs a Growing Neural Gas [3] procedure to derive graph representations of pocket topologies.

Self-organizing map (SOM) projections revealed a limited number of pocket topologies. We argue that there is only a small set of pocket shapes realized in the known ligand-receptor complexes.

References

  1. Balaban AT: Applications of Graph Theory in Chemistry.

    J Chem Inf Comput Sci 1985, 25:334-343. OpenURL

  2. Weisel M, Proschak E, Schneider G: PocketPicker: Analysis of Ligand Binding-Sites with Shape Descriptors.

    Chem Cent J 2007, 1:7. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  3. Fritzke B: Growing cell structures – a selforganizing network for unsupervised and supervised learning.

    Neural Networks 1994, 7:1441-1460. Publisher Full Text OpenURL