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PocketGraph: graph representation of binding site volumes

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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. 1.

    Balaban AT: Applications of Graph Theory in Chemistry. J Chem Inf Comput Sci. 1985, 25: 334-343.

  2. 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.

  3. 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.

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Correspondence to M Weisel.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Keywords

  • Graph Representation
  • Complex Representation
  • Abstract Description
  • Common Technique
  • Molecular Graph