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  • Poster presentation
  • Open Access

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

  • 1Email author,
  • 2,
  • 2 and
  • 1
Journal of Cheminformatics20124 (Suppl 1) :P34

  • Published:


  • Virtual Screening
  • Protein Conformation
  • Simulation Trajectory
  • Docking Programme
  • Docking Experiment

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.

Authors’ Affiliations

School of Chemistry, University of Southampton, Highfield, Southampton, SO17 1BJ, UK
Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK


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© Sander et al; licensee BioMed Central Ltd. 2012

This article is published under license to 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.