Prediction of ligand binding affinity using a multiple-conformations-multiple-protonation scheme: application to estrogen receptor α.

A fast method that can predict the binding affinities of chemicals to a target protein with a high degree of accuracy will be very useful in drug design and regulatory science. We have been developing a scoring function for affinity prediction, which can be applied to extensive protein systems, and also trying to generate a prediction scheme that specializes in each target protein, with as high a predictive power as possible. In this study, we have constructed a prediction scheme with target-specific scores for estimating ligand-binding affinities to human estrogen receptor α (ERα), considering the major conformational change between agonist- and antagonist-bound forms and the change in protonation states of histidine at the ligand-binding site. The generated scheme calibrated with fewer training compounds (23 for the agonist-bound form, 17 for the antagonist-bound form) demonstrated good predictive power (a predictive r(2) of 0.83 for 154 validation compounds); this was also true for compounds with frameworks that were quite different from those of the training compounds. Our prediction scheme will be useful in drug development targeting ERα and in primary screening of endocrine disruptors, and provides a successful method of affinity prediction considering the major conformational changes in a protein.