Experimental versus predicted affinities for ligand binding to estrogen receptor: Iterative selection and rescoring of docked poses systematically improves the correlation

  • Wright J
  • Anderson J
  • Shadnia H
 et al. 
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The computational determination of binding modes for a ligand into a protein receptor is much more successful than the prediction of relative binding affinities (RBAs) for a set of ligands. Here we consider the binding of a set of 26 synthetic A-CD ligands into the estrogen receptor ERα. We show that the MOE default scoring function (London dG) used to rank the docked poses leads to a negligible correlation with experimental RBAs. However, switching to an energy-based scoring function, using a multiple linear regression to fit experimental RBAs, selecting top-ranked poses and then iteratively repeating this process leads to exponential convergence in 4-7 iterations and a very strong correlation. The method is robust, as shown by various validation tests. This approach may be of general use in improving the quality of predicted binding affinities.

Author-supplied keywords

  • Docking
  • Estrogen receptor
  • Iterative rescoring
  • Scoring

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  • James S. Wright

  • James M. Anderson

  • Hooman Shadnia

  • Tony Durst

  • John A. Katzenellenbogen

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