title: Experimental versus predicted affinities for ligand binding to estrogen receptor: iterative selection and rescoring of docked poses systematically improves the correlation.
abstract: 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.
authors: Department of Chemistry, Carleton University, 1125 Colonel By Dr., Ottawa, K1S 5B6, Canada, jim_wright@carleton.ca.
authors: James S
authors: JS
authors: Wright
authors: James M
authors: JM
authors: Anderson
authors: Hooman
authors: H
authors: Shadnia
authors: Tony
authors: T
authors: Durst
authors: John A
authors: JA
authors: Katzenellenbogen
mesh_terms: D001665:Binding Sites; D047628:Estrogen Receptor alpha / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D006860:Hydrogen Bonding; D008024:Ligands; D062105:Molecular Docking Simulation; D011485:Protein Binding; D013816:Thermodynamics
chemical_list: C506487:ESR1 protein, human; D047628:Estrogen Receptor alpha; D008024:Ligands
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