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

Open Access Poster presentation

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: http://www.jcheminf.com/content/4/S1/P34


Published:1 May 2012

© 2012 Sander et al; licensee BioMed Central Ltd.

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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