This article is part of the supplement: 7th German Conference on Chemoinformatics: 25 CIC-Workshop

Open Access Poster presentation

Novel binding pocket descriptors based on DrugScore potential fields encoded by 3D Zernike descriptors

Britta Nisius*, Fan Sha and Holger Gohlke

  • * Corresponding author: Britta Nisius

Author Affiliations

Department of Mathematics and Natural Sciences, Institute of Pharmaceutical and Medicinal Chemistry, Heinrich-Heine University, Universitätsstraße 1, 40225 Düsseldorf, Germany

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Journal of Cheminformatics 2012, 4(Suppl 1):P28 doi:10.1186/1758-2946-4-S1-P28


The electronic version of this article is the complete one and can be found online at: http://www.jcheminf.com/content/4/S1/P28


Published:1 May 2012

© 2012 Nisius et al; licensee BioMed Central Ltd.

Poster presentation

Proteins interact with other molecules, e.g. ligands or other proteins, in specific binding sites. Key factors for these interactions are the shape, size, and buriedness of the binding site, as well as its physicochemical composition. Since all these properties usually significantly vary among different proteins, up to now there is no standard definition what constitutes a binding site [1]. Thus, novel pocket descriptors allowing an in-depth characterization of binding sites are highly desired.

Hence, we developed novel binding pocket descriptors based on 3D molecular interaction fields. The binding pocket of a protein is characterized using the distance dependent, knowledge-based pair potentials of the DrugScore scoring function [2] in combination with multiple ligand atom probes. To allow an efficient comparison of the resulting potential fields, the 3D grids are encoded using 3D Zernike descriptors.

The 3D Zernike polynomials Znlm are orthonormal basis functions on the unit sphere. Thus, any 3D object can be represented as:

using a 3D Zernike function expansion. We utilized the resulting function expansion coefficients cnlm, i.e. the 3D Zernike moments, to describe the 3D molecular potential fields characterizing a protein's binding pocket.

The resulting descriptors are invariant under rotation, scaling, and translation and enable a fast comparison and an efficient characterization of protein binding pockets. Thus, these novel pocket descriptors can be used to predict target druggability or to calculate similarities between binding pockets, e.g. to predict potential off-targets or to perform protein function de-orphanization.

References

  1. Perot S, Sperandio O, Miteva MA, Camproux AC, Villoutreix BO: Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery.

    Drug Discov Today 2010, 15:656-667. PubMed Abstract | Publisher Full Text OpenURL

  2. Gohlke H, Hendlich M, Klebe G: Knowledge-based Scoring Function to Predict Protein-Ligand Interactions.

    J Mol Biol 2000, 295:337-356. PubMed Abstract | Publisher Full Text OpenURL