QSAR and mechanistic interpretation of estrogen receptor binding

A multi-dimensional formulation of the COmmon REactivity PAttern (COREPA) modeling approach has been used to investigate chemical binding to the human estrogen receptor (hER). A training set of 645 chemicals included 497 steroid and environmental chemicals (database of the Chemical Evaluation and Research Institute, Japan - CERI) and 148 chemicals to further explore hER-structure interactions (selected J. Katzenellenbogen references). Upgrades of modeling approaches were introduced for multivariate COREPA analysis, optimal conformational generation and description of the local hydrophobicity of chemicals. Analysis of reactivity patterns based on the distance between nucleophilic sites resulted in identification of distinct interaction types: a steroid-like A–B type described by frontier orbital energies and distance between nucleophilic sites with specific charge requirements; an A–C type where local hydrophobic effects are combined with electronic interactions to modulate binding; and mixed A–B–C (AD) type. Chemicals were grouped by type, then COREPA models were developed for within specific relative binding affinity ranges of >10%, 10 > RBA ≥ 0.1%, and 0.1 > RBA > 0.0%. The derived models for each interaction type and affinity range combined specific prefiltering requirements (interatomic distances) and a COREPA classification node using no more than 2 discriminating parameters. The interaction types are becoming less distinct in the lowest activity range for each chemicals of each type; here, the modeling was performed within chemical classes (phenols, phthalates, etc.). The ultimate model was organized as a battery of local models associated to interaction type and mechanism. §Presented at the 12th International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology (QSAR2006), 8–12 May 2006, Lyon, France.

[1]  Dimitar Dimitrov,et al.  Conformational Coverage by a Genetic Algorithm , 1999, J. Chem. Inf. Comput. Sci..

[2]  Nina Nikolova,et al.  COREPA-M: A Multi-Dimensional Formulation of COREPA , 2004 .

[3]  Ovanes Mekenyan,et al.  Dynamic 3D QSAR techniques: applications in toxicology , 2003 .

[4]  J. Katzenellenbogen,et al.  Molecular structures, conformational analysis, and preferential modes of binding of 3-aroyl-2-arylbenzo[b]thiophene estrogen receptor ligands: LY117018 and aryl azide photoaffinity labeling analogs. , 1993, Journal of medicinal chemistry.

[5]  T W Schultz,et al.  ‘Dynamic’ QSAR For Semicarbazide‐induced Mortality in Frog Embryos , 1996, Journal of applied toxicology : JAT.

[6]  B. Weintraub Molecular endocrinology : basic concepts and clinical correlations , 1995 .

[7]  J. Katzenellenbogen,et al.  2-Arylindenes and 2-arylindenones: molecular structures and considerations in the binding orientation of unsymmetrical nonsteroidal ligands to the estrogen receptor. , 1989, Journal of medicinal chemistry.

[8]  P. Jurs,et al.  Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies , 1990 .

[9]  M. Mizutani,et al.  Rational automatic search method for stable docking models of protein and ligand. , 1994, Journal of molecular biology.

[10]  F. Young Biochemistry , 1955, The Indian Medical Gazette.

[11]  John A. Katzenellenbogen,et al.  The estradiol pharmacophore: Ligand structure-estrogen receptor binding affinity relationships and a model for the receptor binding site , 1997, Steroids.

[12]  Gilman D. Veith,et al.  Dynamic QSAR: A New Search for Active Conformations and Significant Stereoelectronic Indices , 1994 .

[13]  J. Katzenellenbogen,et al.  Photoaffinity labels for estrogen binding proteins of rat uterus. , 1973, Biochemistry.

[14]  Dynamic QSAR: least squares fits with multiple predictors , 1997 .

[15]  B. Katzenellenbogen,et al.  The nature of the ligand-binding pocket of estrogen receptor alpha and beta: The search for subtype-selective ligands and implications for the prediction of estrogenic activity , 2003 .

[16]  James J. P. Stewart,et al.  MOPAC: A semiempirical molecular orbital program , 1990, J. Comput. Aided Mol. Des..

[17]  S. Sheather Density Estimation , 2004 .

[18]  T. Wiese,et al.  Molecular modeling of steroidal estrogens: Novel conformations and their role in biological activity , 1994, The Journal of Steroid Biochemistry and Molecular Biology.

[19]  J. Sumpter,et al.  Estrogenicity of alkylphenolic compounds: A 3‐D structure—activity evaluation of gene activation , 2000 .

[20]  G. Ankley,et al.  A computationally based identification algorithm for estrogen receptor ligands: part 2. Evaluation of a hERalpha binding affinity model. , 2000, Toxicological sciences : an official journal of the Society of Toxicology.

[21]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[22]  E. Eliel Chemistry in Three Dimensions , 1993 .

[23]  David W. Scott,et al.  Multivariate Density Estimation: Theory, Practice, and Visualization , 1992, Wiley Series in Probability and Statistics.

[24]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[25]  Q Xie,et al.  Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens. , 2001, Chemical research in toxicology.

[26]  Gerald T. Ankley,et al.  A Computationally-Based Hazard Identification Algorithm That Incorporates Ligand Flexibility. 1. Identification of Potential Androgen Receptor Ligands , 1997 .

[27]  B. Matthews,et al.  Response of a protein structure to cavity-creating mutations and its relation to the hydrophobic effect. , 1992, Science.

[28]  Gergana Dimitrova,et al.  A Stepwise Approach for Defining the Applicability Domain of SAR and QSAR Models , 2005, J. Chem. Inf. Model..

[29]  Wendy A. Warr,et al.  Chemical Structures , 1988 .

[30]  Gilman D. Veith,et al.  QSAR prioritization of chemical inventories for endocrine disruptor testing , 2003 .

[31]  M. L. Connolly Analytical molecular surface calculation , 1983 .

[32]  G. Habermehl,et al.  ReviewPure appl. Chem: Rinehart, K. L., et al. Marine natural products as sources of antiviral, antimicrobial, and antineoplastic Agents. 53, 795 (1981). (K. L. Rinehart, University of Illinois, Urbana, IL 61801, U.S.A.) , 1983 .

[33]  O Mekenyan,et al.  A computationally based identification algorithm for estrogen receptor ligands: part 1. Predicting hERalpha binding affinity. , 2000, Toxicological sciences : an official journal of the Society of Toxicology.

[34]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[35]  Ovanes Mekenyan,et al.  2D-3D Migration of Large Chemical Inventories with Conformational Multiplication. Application of the Genetic Algorithm , 2005, J. Chem. Inf. Model..