title: CoMFA and docking study of novel estrogen receptor subtype selective ligands.
abstract: We present the results from a Comparative Molecular Field Analysis (CoMFA) and docking study of a diverse set of 36 estrogen receptor ligands whose relative binding affinities (RBA) with respect to 17beta-Estradiol were available in both isoforms of the nuclear estrogen receptors (ER alpha, ER beta). Initial CoMFA models exhibited a correlation between the experimental relative binding affinities and the molecular steric and electrostatic fields; ER alpha: r2 = 0.79, q2 = 0.44 ER beta: r2 = 0.93, q2 = 0.63. Addition of the solvation energy of the isolated ligand improved the predictive nature of the ER beta model initially; r2 = 0.96, q2 = 0.70 but upon rescrambling of the data-set and reselecting the training set at random, inclusion of the ligand solvation energy was found to have little effect on the predictive nature of the CoMFA models. The ligands were then docked inside the ligand binding domain (LBD) of both ER alpha and ER beta utilizing the docking program Gold, after-which the program CScore was used to rank the resulting poses. Inclusion of both the Gold and CScore scoring parameters failed to improve the predictive ability of the original CoMFA models. The subtype selectivity expressed as RBA(ER alpha/ER beta) of the test sets was predicted using the most predictive CoMFA models, as illustrated by the cross-validated r2. In each case the most selective ligands were ranked correctly illustrating the utility of this method as a prescreening tool in the development of novel estrogen receptor subtype selective ligands.
authors.affiliation: Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 South Kingshighway, Campus Box 8225, St. Louis, MO 63110, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Wolohan
authors.forename: David E
authors.initials: DE
authors.lastname: Reichert
mesh_terms: D001665:Binding Sites; D001667:Binding, Competitive; D003198:Computer Simulation; D030562:Databases, Protein; D047628:Estrogen Receptor alpha; D047629:Estrogen Receptor beta; D004967:Estrogens / Q000737:chemistry / Q000378:metabolism / Q000494:pharmacology; D004968:Estrogens, Non-Steroidal / Q000737:chemistry / Q000378:metabolism / Q000494:pharmacology; D005663:Furans / Q000737:chemistry; D006860:Hydrogen Bonding; D021621:Imaging, Three-Dimensional; D008024:Ligands; D008956:Models, Chemical; D008958:Models, Molecular; D008968:Molecular Conformation; D015394:Molecular Structure; D011487:Protein Conformation; D011720:Pyrazoles / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D011960:Receptors, Estrogen / Q000819:agonists* / Q000737:chemistry* / Q000378:metabolism; D055672:Static Electricity; D013816:Thermodynamics
chemical_list: D047628:Estrogen Receptor alpha; D047629:Estrogen Receptor beta; D004967:Estrogens; D004968:Estrogens, Non-Steroidal; D005663:Furans; D008024:Ligands; D011720:Pyrazoles; D011960:Receptors, Estrogen