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

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The assessment of computationally derived protein ensembles in protein-ligand docking

Barbara Sander1*, Oliver Korb2, Jason Cole2 and Jonathan W Essex1

Author Affiliations

1 School of Chemistry, University of Southampton, Highfield, Southampton, SO17 1BJ, UK

2 Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK

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Journal of Cheminformatics 2012, 4(Suppl 1):P34  doi:10.1186/1758-2946-4-S1-P34

The electronic version of this article is the complete one and can be found online at:

Published:1 May 2012

© 2012 Sander et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

The inclusion of receptor flexibility in protein-ligand docking experiments has become a major research interest in drug discovery [1,2]. One of the possible methods applied is the use of multiple discrete protein conformations, so called ensemble docking [3,4]. With computational techniques like Molecular Dynamics (MD) a large number of different conformations can be generated, not all of which can or should be included in the docking or virtual screening process [5]. The question arises if and how suitable protein conformations can be selected systematically a priori based on quantifiable conformational features.

For neuraminidase and cyclin-dependent kinase II (CDK2), snapshots of MD simulation trajectories have been clustered based on different structural properties using a variety of clustering methods. To establish a possible correlation between docking performance and target conformational attributes the clustered snapshots have been subjected to extensive self- and cross-docking experiments as well as virtual screening using the GOLD docking programme. It is shown that conformationally similar snapshots do not necessarily result in a similar docking or virtual screening performance. The selection of the particular structural property on which to base the clustering appears to be the essential problem.


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