- Poster presentation
- Open Access
PocketGraph: graph representation of binding site volumes
https://doi.org/10.1186/1752-153X-3-S1-P66
© Weisel et al; licensee BioMed Central Ltd. 2009
- Published: 05 June 2009
Keywords
- Graph Representation
- Complex Representation
- Abstract Description
- Common Technique
- Molecular Graph
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.
Authors’ Affiliations
References
- Balaban AT: Applications of Graph Theory in Chemistry. J Chem Inf Comput Sci. 1985, 25: 334-343.View ArticleGoogle Scholar
- 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.View ArticleGoogle Scholar
- 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.View ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd.
