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Oxford, United Kingdom

Ross G.A.,University of Oxford | Morris G.M.,Inhibox Inc. | Biggin P.C.,University of Oxford
PLoS ONE | Year: 2012

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity. © 2012 Ross et al.

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Introduction Ligand-protein docking methods attempt to identify optimal positions, orientations and conformations of a ligand or small molecule with respect to a given protein receptor or enzyme. InhibOx offers extensive expertise and a range of solutions in ligand-protein docking ...

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