Mechanism-based modeling of estrogen receptor binding affinity a common reactivity pattern (COREPA) implementation

Endocrine Disruption Modeling

Volumn , Issue , 2009, Pages 259-293

  Mekenyan, Ovanes ^a   Serafimova, Rossitsa ^a  

^a ASSEN ZLATAROV UNIVERSITY   (Bulgaria)

Author keywords

Common reactivity pattern (COREPA); Estrogen receptor (ER) binding affinity; Interaction mechanism; Molecular flexibility

Indexed keywords

EID: 80052792710     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/9781420076363     Document Type: Chapter

References (44)

1. OECD, Annexes to the Report on Principles for Establishing the Status of Development and Validation of (Quantitative) Structure-Activity Relationships [(Q)SARs], ENV/JM/TG(2004)27/ANN, 2004.
2. N. Ing and B. O’Malley, The steroid hormone receptor superfamily — Molecular mechanisms of action, in Molecular Endocrinology: Basic Concepts and Clinical Correlations, B. Weintraub, ed., Raven Press, New York, 1995, pp. 195-215.
3. OECD, Final Report of the Sixth Meeting of the Task Force on Endocrine 530 Disrupters Testing and Assessment (EDTA 6), ENV/JM/TG/EDTA/M, 2003.
4. M. Mizutani, N. Tomioka, and A. Itai, Rational automatic search method for stable docking models of protein and ligand, J. Mol. Biol. 243 (1994), pp. 310-326.
5. R. Cramer, D. Patterson, and J. Bunce, Comparative molecular field analysis (CoMFA): Effect of shape on binding of steroids to carrier proteins, J. Am. Chem. Soc. 110 (1988), pp. 5959-5967.
6. L. Xing, W. Welsh, W. Tong, R. Perkins, and D. Sheehan, Comparison of estrogen receptor alpha and beta subtypes based on comparative molecular field analysis (CoMFA), SAR QSAR Environ. Res. 10 (1999), pp. 215-238.
7. T. Gantchev, H. Ali, and J van Lier, Quantitative-structure-activity relationships/comparative molecular field analysis (QSAR/CoMFA) for receptor-binding properties of halogenated estradiol derivates, J. Med. Chem. 37 (1994), pp. 4164-4176.
8. P. Schmieder, A. Aptula, E. Routledge, J. Sumpter, and O. Mekenyan, Estrogenicity of alkylphenolic compounds: A 3-D structure-activity evaluation of geneactivation, Environ. Toxicol. Chem. 19 (2000), pp.1727-1740.
9. O. Mekenyan, N. Nikolova, and P. Schmieder, Dynamic 3D QSAR techniques: Application in toxicology, J. Mol. Struct. (THEOCHEM) 622 (2003), pp.147-165.
10. P. Schmieder, O. Mekenyan, S. Bradbury, and G. Veith, QSAR prioritization of chemical inventories for endocrine disruptor testing, Pure Appl. Chem. 75 (2003), pp. 2389-2396.
11. E.L. Eliel, Chemistry in three dimensions, in Chemical Structures, W. Warr, ed., Springer, Berlin, 1993, pp. 1-8.
12. O. Mekenyan, J. Ivanov, G. Veith, and S. Bradbury, Dynamic QSAR: A new search for active conformations and significant stereoelectronic indices, Quant. Struct. Act. Relat. 13 (1994), pp. 302-307.
13. O. Mekenyan, G. Veith, D. Call, and G. Ankley, A QSAR evaluation of Ah receptors binding of halogenated aromatic xenobiotics, Environ. Helth. Perspect. 104 (1996), pp. 1302-1310.
14. O. Mekenyan, T. Schultz, G. Veith, and V. Kamenska, “Dynamic” QSAR for semicarbazide-induced mortality in frog embryo, J. Appl. Toxicol. 16 (1996), pp. 355-363.
15. S. Bradbury, O. Mekenyan, and G. Ankley, Quantitative Structure-Activity Relationships for polychlorinated hydroxybiphenyl estrogen receptor binding affinity: An assessment of conformer flexibility, Environ. Chem. Toxicol. 15 (1996), pp. 1945-1954.
16. S. Bradbury, O. Mekenyan, and G. Ankley, The role of ligand flexibility in predicting biological activity: Structural-activity relationship for aryl hydrocarbon, estrogen and androgen receptor binding affinity, Environ. Sci. Technol. 17 (1998), pp. 15-25.
17. T. Wiese and S. Brooks, Molecular modeling of steroidal estrogens: Novel conformations and their role in biological activity, J. Steroid Biochem. Mol. Biol. 50 (1994), pp. 61-73.
18. S. Dimitrov and O. Mekenyan, Dynamic QSAR: Least squares fit with multiple predictors, Chemom. Intell. Lab. Syst. 39 (1997), pp. 1-9.
19. O. Mekenyan, J. Ivanov, S. Karabunarliev, S. Bradbury, G. Ankley, and W. Karcher, A computationally based hazard identification algorithm that incorporates ligand flexibility, Environ. Sci. Technol. 31 (1997), pp. 3702-3711.
20. S. Bradbury, V. Kamenska, P. Schmieder, G. Ankley, and O. Mekenyan, A computationally based identification algorithm for estrogen receptor ligands, Part I. Predicting hER binding affinity, Toxicol. Sci. 58 (2000), pp. 253-269.
21. O. Mekenyan, V. Kamenska, P. Schmieder, G. Ankley, and S. Bradbury, Computationally based identification algorithm for estrogen receptor ligands, Part II. Evaluation of a hER binding affinity model, Toxicol. Sci. 58 (2000), pp. 270-281.
22. O. Mekenyan, D. Dimitrov, N. Nikolova, and S. Karabunarliev, Conformational coverage by a genetic algorithm, Chem. Inf. Comput. Sci. 39 (1999), pp. 997-1016.
23. T. Pavlov, M. Todorov, G. Stoyanova, P. Schmieder, H. Aladjov, R. Serafimova, and O. Mekenyan, Conformational coverage by a genetic algorithm: Saturation of conformational space, J. Chem. Inf. Model. 47 (2007), pp. 851-863.
24. M. Shelby, R. Newbold, D. Tully, K. Chae, and V. Davis, Assessing environmental chemicals for estrogenicity using a combination of in vitro and in vivo assays. Environ. Health Perspect. 104 (1996), pp. 1296-1300.
25. J. Katzenellenbogen, R. Muthyala, and B. Katzenellenbogen, Nature of the ligandbinding pocket of estrogen receptor α and β: The search for subtype-selective ligands and implications for the prediction of estrogenic activity, Pure Appl. Chem. 75 (2003), pp. 2397-2403.
26. G. Anstead, K. Carlson, and J. Katzenellenbogen, The estradiol pharmacophore: Ligand structure-estrogen receptor binding affinity relationships and a model for the receptor binding site, Steroids 62 (1997), pp. 268-303.
27. R. Serafimova, M. Todorov, D. Nedelcheva, T. Pavlov, Y. Akahori, M. Nakai, and O. Mekenyan, QSAR and mechanistic interpretation of estrogen receptor binding, SAR QSAR Environ. Res. 18 (2007), pp. 389-421.
28. METI, Current status of testing methods development for endocrine disrupters, Sixth Meeting of the Task Force on Endocrine Disrupters Testing and Assessment (EDTA), Tokyo, 2002. Available at www.meti.go.jp/english/report/data/gEndocappendix1e.pdf.
29. J. Surfin, D. Dunn, and G. Marshall, Steric mapping of the L-menthionine binding site of AYP-L-mentionine-S-adenosyl-transferase, Molec. Pharmacol. 19 (1981), pp. 307-313.
30. J. Katzenellenbogen, Effectiveness of QSAR for prescreening of endocrine disruptor hazard, SCOPE/IUPAC International Symposium on Endocrine Active Substances, Yokohama, Japan, 2002.
31. SYBYL, Tripos Associates Inc., St, Louis, MO.
32. R. Dammkoehler, S. Karasek, E. Shands, and G. Marshall, Constrained search of conformational hyperspace, J. Comput-Aided Mol. Des. 3 (1989), pp. 3-21.
33. M. Lipton and W. Still, The multiple minimum problem in molecular modeling. Tree searching internal coordinate conformational space, J. Comput. Chem. 9 (1988), pp. 343-355.
34. O. Mekenyan, T. Pavlov, V. Grancharov, P. Schmieder, and G. Veith, 2D-3D migration of large chemical inventories with conformational multiplication. Application of the genetic algorithm, J. Chem. Inf. Model. 45 (2005), pp. 283-292.
35. J. Steward, MOPAC: A semiempirical molecular orbital program, J. Comput.-Aided Mol. Des. 4 (1990), pp. 1-105.
36. J. Steward, MOPAC 93. Fujitsu Limited, 9-3, Nakase 1-Chome, Mihama-ku. Chiba-City, Chiba 261, Japan and Stewart Computational Chemistry, 15210 Paddington Circle, Colorado Springs, CO.
37. M. Connoly, Analytical molecular surface calculation, J. Appl. Cristallogr. 16 (1983), pp. 548-558.
38. D. Stanton and P. Jurs, Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies, Anal. Chem. 62 (1990), pp. 2323-2329.
39. O. Mekenyan, N. Nikolova, P. Schmieder, and G. Veith, COREPA-M: A multi-dimensional formulation of COREPA, QSAR Comb. Sci. 23 (2004), pp. 5-18.
40. S. Dimitrov, G. Dimitrova, T. Pavlov, N. Dimitrova, G. Patlewicz, J. Niemela, and O. Mekenyan, A stepwise approach for defining the applicability domain of SAR and QSAR models, J. Chem. Inf. Mod. 45 (2005), pp. 839-849.
41. O. Mekenyan, S. Dimitrov, R. Serafimova, E. Thompson, S. Kotov, N. Dimitrova, and J. Walker, Identification of the structural requirements for mutagenicity by incorporating molecular flexibility and metabolic activation of chemicals I: TA100 model, Chem. Res.Toxicol. 17 (2004), pp. 753-766.
42. O. Mekenyan, S. Dimitrov, T. Pavlov, and G. Veith, A systematic approach to stimulating metabolism in computational toxicology. I. The TIMES heuristic modelling framework, Curr. Pharm. Des. 10 (2004), pp. 1273-1293.
43. R. Serafimova, M. Todorov, S. Kotov, E. Jacob, N. Aptula, and O. Mekenyan, Identification of the structural requirements for mutagencitiy, by incorporating molecular flexibility and metabolic activation of chemicals II. General Ames mutagenicity model, Chem. Res. Toxicol. 20 (2007), pp. 662-676.
44. P. Schmieder, M. Tupper, and R. Kolanczyk, unpublished results, U.S. EPA, ORD, NHEERL, Mid-Continent Ecology Division, Duluth, MN.