PocketPicker: analysis of ligand binding-sites with shape descriptors

Background Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of su
Background Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding. Results We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITEcs, PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITEcs and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites. Conclusions The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections.
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Metadaten
Author:Martin Weisel, Ewgenij Proschak, Gisbert Schneider
URN:urn:nbn:de:hebis:30-38843
URL:http://journal.chemistrycentral.com/content/1/1/7
DOI:http://dx.doi.org/10.1186/1752-153X-1-7
Parent Title (English):Chemistry Central Journal
Publisher:BioMed Central
Place of publication:London
Document Type:Article
Language:English
Date of Publication (online):2007/04/04
Year of first Publication:2007
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2007/04/04
Volume:1
Issue:7
Pagenumber:17
Note:
© 2007 Weisel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
HeBIS PPN:291867189
Institutes:Biowissenschaften
Dewey Decimal Classification:570 Biowissenschaften; Biologie
Sammlungen:Universitätspublikationen
Sondersammelgebiets-Volltexte
Licence (German):License LogoCreative Commons - Namensnennung 2.0

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