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Autogrid-based clustering of kinases: selection of representative conformations for docking purposes

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Abstract

The selection of the most appropriate protein conformation is a crucial aspect in molecular docking experiments. In order to reduce the errors arising from the use of a single protein conformation, several authors suggest the use of several tridimensional structures for the target. However, the selection of the most appropriate protein conformations still remains a challenging goal. The protein 3D-structures selection is mainly performed based on pairwise root-mean-square-deviation (RMSD) values computation, followed by hierarchical clustering. Herein we report an alternative strategy, based on the computation of only two atom affinity map for each protein conformation, followed by multivariate analysis and hierarchical clustering. This methodology was applied on seven different kinases of pharmaceutical interest. The comparison with the classical RMSD-based strategy was based on cross-docking of co-crystallized ligands. In the case of epidermal growth factor receptor kinase, also the docking performance on 220 known ligands were evaluated, followed by 3D-QSAR studies. In all the cases, the herein proposed methodology outperformed the RMSD-based one.

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Notes

  1. At this stage, the RMSD value should be used to compare docked and real poses only if the docking studies were performed on a single protein conformation.

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Acknowledgments

The present work has been carried out with the financial support of the University of Padova ‘Progetto Giovani Studiosi 2012’ to G.M. A.F. thanks financial supports from the University of Padova for a PhD student grant; G.M. thanks financial support from the University of Padova for a post-doc senior grant.

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  1. Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131 , Padova, Italy

    Giovanni Marzaro, Alessandro Ferrarese & Adriana Chilin

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  1. Giovanni Marzaro
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  2. Alessandro Ferrarese
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  3. Adriana Chilin
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Correspondence to Giovanni Marzaro.

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Marzaro, G., Ferrarese, A. & Chilin, A. Autogrid-based clustering of kinases: selection of representative conformations for docking purposes. Mol Divers 18, 611–619 (2014). https://doi.org/10.1007/s11030-014-9524-8

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