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Open Access Research article

Setting up a large set of protein-ligand PDB complexes for the development and validation of knowledge-based docking algorithms

Luis A Diago1, Persy Morell2, Longendri Aguilera2 and Ernesto Moreno3*

Author Affiliations

1 Department of Bioengineering, Faculty of Electrical Engineering, Havana Institute of Technology, Havana 19390, Cuba

2 Faculty of Bioinformatics, University of Information Science, Havana 19370, Cuba

3 Center of Molecular Immunology, P.O. Box 16040, Havana 11600, Cuba

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BMC Bioinformatics 2007, 8:310  doi:10.1186/1471-2105-8-310

Published: 25 August 2007

Abstract

Background

The number of algorithms available to predict ligand-protein interactions is large and ever-increasing. The number of test cases used to validate these methods is usually small and problem dependent. Recently, several databases have been released for further understanding of protein-ligand interactions, having the Protein Data Bank as backend support. Nevertheless, it appears to be difficult to test docking methods on a large variety of complexes. In this paper we report the development of a new database of protein-ligand complexes tailored for testing of docking algorithms.

Methods

Using a new definition of molecular contact, small ligands contained in the 2005 PDB edition were identified and processed. The database was enriched in molecular properties. In particular, an automated typing of ligand atoms was performed. A filtering procedure was applied to select a non-redundant dataset of complexes. Data mining was performed to obtain information on the frequencies of different types of atomic contacts. Docking simulations were run with the program DOCK.

Results

We compiled a large database of small ligand-protein complexes, enriched with different calculated properties, that currently contains more than 6000 non-redundant structures. As an example to demonstrate the value of the new database, we derived a new set of chemical matching rules to be used in the context of the program DOCK, based on contact frequencies between ligand atoms and points representing the protein surface, and proved their enhanced efficiency with respect to the default set of rules included in that program.

Conclusion

The new database constitutes a valuable resource for the development of knowledge-based docking algorithms and for testing docking programs on large sets of protein-ligand complexes. The new chemical matching rules proposed in this work significantly increase the success rate in DOCKing simulations. The database developed in this work is available at http://cimlcsext.cim.sld.cu:8080/screeningbrowser/ webcite.