A computationally based identification algorithm for estrogen receptor ligands: part 1. Predicting hERalpha binding affinity.
The common reactivity pattern (COREPA) approach is a 3-dimensional, quantitative structure activity relationship (3-D QSAR) technique that permits identification and quantification of specific global and local stereoelectronic characteristics associated with a chemical's biological activity. It goes beyond conventional 3-D QSAR approaches by incorporating dynamic chemical conformational flexibility in ligand-receptor interactions. The approach provides flexibility in screening chemical data sets in that it helps establish criteria for identifying false positives and false negatives, and is not dependent upon a predetermined and specified toxicophore or an alignment of conformers to a lead compound. The algorithm was recently used to screen chemical data sets for rat androgen receptor binding affinity. To further explore the potential application of the algorithm in establishing reactivity patterns for human estrogen receptor alpha (hERalpha) binding affinity, the stereoelectronic requirements associated with the binding affinity of 45 steroidal and nonsteroidal ligands to the receptor were defined. Reactivity patterns for relative hERalpha binding affinity (RBA; 17ss-estradiol = 100%) were established based on global nucleophilicity, interatomic distances between electronegative heteroatoms, and electron donor capability of heteroatoms. These reactivity patterns were used to establish descriptor profiles for identifying and ranking compounds with RBA of > 150%, 100-10%, 10-1%, and 1-0.1%. Increasing specificity of reactivity patterns was detected for ligand data sets with RBAs above 10%. Using the results of this analysis, an exploratory expert system was developed for use in ranking relative ER binding affinity potential for large chemical data sets.
Bradbury
S
S
U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Boulevard, Duluth, Minnesota 55804, USA. bradbury.steven@epa.gov
Kamenska
V
V
Schmieder
P
P
Ankley
G
G
Mekenyan
O
O
D000465:Algorithms; D000818:Animals; D001943:Breast Neoplasms / Q000378:metabolism; D003663:Decision Trees; D047628:Estrogen Receptor alpha; D005260:Female; D006801:Humans; D008024:Ligands; D008433:Mathematics; D051379:Mice; D008954:Models, Biological; D011487:Protein Conformation; D021281:Quantitative Structure-Activity Relationship; D051381:Rats; D011960:Receptors, Estrogen / Q000737:chemistry / Q000378:metabolism*; D014407:Tumor Cells, Cultured; D014599:Uterus / Q000378:metabolism
D047628:Estrogen Receptor alpha; D008024:Ligands; D011960:Receptors, Estrogen