Chemistry Central Journal

© Weisel et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.

10.1186/1752-153X-3-S1-P66

Poster presentation

PocketGraph: graph representation of binding site volumes

M Weisel1 , J Kriegl¹ and G Schneider¹

(1)

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

Published online: 05 June 2009

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.CrossRef

2.

Weisel M, Proschak E, Schneider G: PocketPicker: Analysis of Ligand Binding-Sites with Shape Descriptors. Chem Cent J. 2007, 1: 7-10.1186/1752-153X-1-7.CrossRef

3.

Fritzke B: Growing cell structures – a selforganizing network for unsupervised and supervised learning. Neural Networks. 1994, 7: 1441-1460. 10.1016/0893-6080(94)90091-4.CrossRef