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  • Title: Comparison of estrogen receptor alpha and beta subtypes based on comparative molecular field analysis (CoMFA).
    Author: Xing L, Welsh WJ, Tong W, Perkins R, Sheehan DM.
    Journal: SAR QSAR Environ Res; 1999; 10(2-3):215-37. PubMed ID: 10491851.
    Abstract:
    A substantial body of evidence indicates that both humans and wildlife suffer adverse health effects from exposure to environmental chemicals that are capable of interacting with the endocrine system. The recent cloning of the estrogen receptor beta subtype (ER-beta) suggests that the selective effects of estrogenic compounds may arise in part by the control of different subsets of estrogen-responsive promoters by the two ER subtypes, ER-alpha and ER-beta. In order to identify the structural prerequisites for ligand-ER binding and to discriminate ER-alpha and ER-beta in terms of their ligand-binding specificities, Comparative Molecular Field Analysis (CoMFA) was employed to construct a three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) model on a data set of 31 structurally-diverse compounds for which competitive binding affinities have been measured against both ER-alpha and ER-beta. Structural alignment of the molecules in CoMFA was achieved by maximizing overlap of their steric and electrostatic fields using the Steric and Electrostatic ALignment (SEAL) algorithm. The final CoMFA models, generated by correlating the calculated 3D steric and electrostatic fields with the experimentally observed binding affinities using partial least-squares (PLS) regression, exhibited excellent self-consistency (r2 > 0.99) as well as high internal predictive ability (q2 > 0.65) based on cross-validation. CoMFA-predicted values of RBA for a test set of compounds outside of the training set were consistent with experimental observations. These CoMFA models can serve as guides for the rational design of ER ligands that possess preferential binding affinities for either ER-alpha or ER-beta. These models can also prove useful in risk assessment programs to identify real or suspected EDCs.
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