Abstract
In target tissue, estrogens initiate a series of events, ending in protein synthesis, by binding to a cytoplasmic receptor. Once the estrogen binds, the receptor complex is activated--a process including dimerization of the estrogen complex with another subunit. In order to study the possible receptor-ligand interactions, conformational analysis was performed on several nonsteroidal estrogen analogs, using the CAMSEQ8 molecular mechanics program.
The probability that a molecule will exist in a given conformational energy well depends both on the energy and the shape of the well. A method has been previously worked out to calculate probabilities using both of these variables, on molecules in which only two torsional angles are rotated (Farnell 1974). A simplification of this method has been used to determine probabilities on molecules where more then two torsional angles are rotated (Kilbourn 1981). This simplification involves taking only one dimension of N-dimensional energy well into account when calculating probabilities. This thesis describes a method of calculating probabilities, on molecules in which more then two bonds are rotated, without making this simplifying assumption. Comparison of this approach with the simplified method demonstrates the importance of taking all N dimensions of an N-dimensional energy well into account when calculating probabilities.
Comparison of the binding data and conformational analysis of diethylstilbestrol (DES) and dimethylstilbestrol (DMS) demonstrates the importance of the ethyl groups to the binding affinity of estrogens to the estrogen receptor. In the analogs with saturated central bonds, binding affinity is not only related to the presence or absence of hydrophobic groups but is also related to the probability that the ligand exists in a planar conformation.
Low energy conformations of DES and meso hexestrol are compared to the x-ray structure of estradiol 17B. The topology of these conformations are studied using contour maps and distance diagrams. These maps and diagrams were constructed using information obtained from a pegboard which measures the three dimensional shape of a molecule. The similarities in these three estrogens, revealed in these studies, suggest the possibility that proteins may completely surround the estrogen.
Based on this idea, two representative models have been proposed to explain possible protein-estrogen and protein-antiestrogen interactions. The sandwich model envisions the estrogen acting as a connecting link between two monomers forming the 5S dimer. In the conformation model an estrogen binds to a 4S estrogen receptor monomer resulting in a protein conformational change which enables the monomer to dimerize.
LLU Discipline
Biochemistry
Department
Biochemistry
School
Graduate School
First Advisor
W. Bart Rippon
Second Advisor
R. Bruce Wilcox
Third Advisor
Charles W. Slattery
Fourth Advisor
David A. Hessinger
Fifth Advisor
Marvin Peters
Degree Name
Doctor of Philosophy (PhD)
Degree Level
Ph.D.
Year Degree Awarded
1982
Date (Title Page)
3-1983
Language
English
Library of Congress/MESH Subject Headings
Estrogens, Synthetic; Estrogens, Non-Steroidal
Type
Dissertation
Page Count
210
Digital Format
Digital Publisher
Loma Linda University Libraries
Copyright
Author
Usage Rights
This title appears here courtesy of the author, who has granted Loma Linda University a limited, non-exclusive right to make this publication available to the public. The author retains all other copyrights.
Recommended Citation
Spady, Robert N., "Conformational Analysis of Nonsteroidal Estrogen Analogs" (1983). Loma Linda University Electronic Theses, Dissertations & Projects. 2501.
https://scholarsrepository.llu.edu/etd/2501
Collection
Loma Linda University Electronic Theses and Dissertations
Collection Website
http://scholarsrepository.llu.edu/etd/
Repository
Loma Linda University. Del E. Webb Memorial Library. University Archives
Included in
Amino Acids, Peptides, and Proteins Commons, Biochemistry Commons, Hormones, Hormone Substitutes, and Hormone Antagonists Commons, Probability Commons