Experimental versus predicted affinities for ligand binding to estrogen receptor: Iterative selection and rescoring of docked poses systematically improves the correlation
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.
|Keywords||Docking, Estrogen receptor, Iterative rescoring, Scoring|
|Journal||Journal of Computer-Aided Molecular Design|
Wright, J.S, Anderson, J.M. (James M.), Shadnia, H. (Hooman), Durst, T. (Tony), & Katzenellenbogen, J.A. (John A.). (2013). Experimental versus predicted affinities for ligand binding to estrogen receptor: Iterative selection and rescoring of docked poses systematically improves the correlation. Journal of Computer-Aided Molecular Design, 27(8), 707–721. doi:10.1007/s10822-013-9670-6