In Silico prediction of estrogen receptor subtype binding affinity and selectivity using statistical methods and molecular docking with 2-arylnaphthalenes and 2-arylquinolines

International Journal of Molecular Sciences

Volumn 11, Issue 9, 2010, Pages 3434-3458

  Wang, Zhizhong ^a   Li, Yan ^b   Ai, Chunzhi ^c   Wang, Yonghua ^a  

^a NORTHWEST A F UNIVERSITY   (China)

^b DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^c DALIAN INSTITUTE OF CHEMICAL PHYSICS   (China)

Author keywords

Docking; QSAR; Receptor; Selectivity

Indexed keywords

2 ARYLNAPHTHALENE; 2 ARYLQUINOLINE; ESTROGEN RECEPTOR; ESTROGEN RECEPTOR ALPHA; ESTROGEN RECEPTOR BETA; LIGAND; NAPHTHALENE DERIVATIVE; QUINOLINE DERIVATIVE; RECEPTOR SUBTYPE; UNCLASSIFIED DRUG;

ARTICLE; BAYES THEOREM; CHEMICAL STRUCTURE; COMPUTER MODEL; CONTROLLED STUDY; DRUG DESIGN; HYDROGEN BOND; HYDROPHOBICITY; LIGAND BINDING; MOLECULAR DOCKING; MULTIPLE REGRESSION; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RECEPTOR AFFINITY; SCREENING; STATISTICAL ANALYSIS;

DOCKING; QSAR; RECEPTOR; SELECTIVITY;

AMINO ACID SEQUENCE; ESTROGEN RECEPTOR ALPHA; ESTROGEN RECEPTOR BETA; MOLECULAR DOCKING SIMULATION; MOLECULAR SEQUENCE DATA; NAPHTHALENES; PROTEIN BINDING; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; QUINOLONES;

EID: 77958452919     PISSN: None     EISSN: 14220067     Source Type: Journal    
DOI: 10.3390/ijms11093434     Document Type: Article

References (47)

1. Gronemeyer, H.; Gustafsson, J.A.; Laudet, V. Principles for modulation of the nuclear receptor superfamily. Nat. Rev. Drug Discov. 2004, 3, 950-964.
2. Horwitz, K.B.; Jackson, T.A.; Bain, D.L.; Richer, J.K.; Takimoto, G.S.; Tung, L. Nuclear receptor coactivators and corepressors. Mol. Endocrinol. 1996, 10, 1167-1177.
3. Nilsson, S.; Gustafsson, J.A. Biological role of estrogen and estrogen receptors. Crit. Rev. Biochem. Mol. Biol. 2002, 37, 1-28.
4. Fitzpatrick, S.L.; Funkhouser, J.M.; Sindoni, D.M.; Stevis, P.E.; Deecher, D.C.; Bapat, A.R.; Merchenthaler, I.; Frail, D.E. Expression of estrogen receptor-beta protein in rodent ovary. Endocrinology 1999, 140, 2581-2591.
5. Kuiper, G.G.; Carlsson, B.; Grandien, K.; Enmark, E.; Haggblad, J.; Nilsson, S.; Gustafsson, J.A. Comparison of the ligand binding specificity and transcript tissue distribution of estrogen receptors alpha and beta. Endocrinology 1997, 138, 863-870.
6. Minutolo, F.; Macchia, M.; Katzenellenbogen, B.S.; Katzenellenbogen, J.A. Estrogen receptor beta ligands: Recent advances and biomedical applications. Med. Res. Rev. 2010, doi:10.1002/med.20186.
7. Zhao, L.Q.; Brinton, R.D. Estrogen receptor beta as a therapeutic target for promoting neurogenesis and preventing neurodegeneration. Drug Dev. Res. 2005, 66, 103-117.
8. Mortensen, D.S.; Rodriguez, A.L.; Sun, J.; Katzenellenbogen, B.S.; Katzenellenbogen, J.A. Furans with basic side chains: Synthesis and biological evaluation of a novel series of antagonists with selectivity for the estrogen receptor alpha. Bioorg. Med. Chem. Lett. 2001, 11, 2521-2524.
9. Mewshaw, R.E.; Edsall, R.J.; Yang, C.J.; Manas, E.S.; Xu, Z.B.; Henderson, R.A.; Keith, J.C.; Harris, H.A. ER beta ligands. 3. Exploiting two binding orientations of the 2-phenylnaphthalene scaffold to achieve ER beta selectivity. J. Med. Chem. 2005, 48, 3953-3979.
10. Gungor, T.; Chen, Y.; Golla, R.; Ma, Z.; Corte, J.R.; Northrop, J.P.; Bin, B.; Dickson, J.K.; Stouch, T.; Zhou, R.; Johnson, S.E.; Seethala, R.; Feyen, J.H. Synthesis and characterization of 3-arylquinazolinone and 3-arylquinazolinethione derivatives as selective estrogen receptor beta modulators. J. Med. Chem. 2006, 49, 2440-2455.
11. Minutolo, F.; Bellini, R.; Bertini, S.; Carboni, I.; Lapucci, A.; Pistolesi, L.; Prota, G.; Rapposelli, S.; Solati, F.; Tuccinardi, T.; Martinelli, A.; Stossi, F.; Carlson, K.E.; Katzenellenbogen, B.S.; Katzenellenbogen, J.A.; Macchia, M. Monoaryl-substituted salicylaldoximes as ligands for estrogen receptor beta. J. Med. Chem. 2008, 51, 1344-1351.
12. Xu, X.; Yang, W.; Li, Y.; Wang, Y.H. Discovery of estrogen receptor modulators: A review of virtual screening and SAR efforts. Exp. Opin. Drug Disc. 2010, 5, 21-31.
13. Waller, C.L. Oprea, T.I.; Chae, K.; Park, H.K.; Korach, K.S.; Laws, S.C.; Wiese, T.E.; Kelce, W.R.; Gray, L.E., Jr. Ligand-based identification of environmental estrogens. Chem. Res. Toxicol. 1996, 9, 1240-1248.
14. Asikainen, A.H.; Ruuskanen, J.; Tuppurainen, K.A. Consensus kNN QSAR: A versatile method for predicting the estrogenic activity of organic compounds in silico. A comparative study with five estrogen receptors and a large, diverse set of ligands. Environ. Sci. Technol. 2004, 38, 6724-6729.
15. Shi, L.M.; Fang, H., Tong, W.D.; Wu, J.; Perkins, R.; Blair, R.M.; Branham, W.S.; Dial, S.L.; Moland, C.I.; Sheehan, D.M. QSAR models using a large diverse set of estrogens. J. Chem. Inf. Comput. Sci. 2001, 41, 186-195.
16. Wolohan, P.; Reichert, D.E. CoMFA and docking study of novel estrogen receptor subtype selective ligands. J. Comput.-Aided Mol. Des. 2003, 17, 313-328.
17. Agatonovic-Kustrin, S.; Turner, J.V.; Glass, B.D. Molecular structural characteristics as determinants of estrogen receptor selectivity. J. Pharm. Biomed. Anal. 2008, 48, 369-375.
18. Barrett, I.; Meegan, M.J.; Hughes, R.B.; Carr, M.; Knox, A.J.; Artemenko, N.; Golfis, G.; Zisterer, D.M.; Lloyd, D.G. Synthesis, biological evaluation, structural-activity relationship, and docking study for a series of benzoxepin-derived estrogen receptor modulators. Bioorg. Med. Chem. 2008, 16, 9554-9573.
19. Kim, K.H.; Greco, G.; Novellino, E. A critical review of recent CoMFA applications. In Perspectives in Drug Discovery and Design; Sprigner: Dordrecht, The Netherlands 1998; pp. 257-315.
20. Sutherland, J.J.; O'Brien, L.A.; Weaver, D.F. A comparison of methods for modeling quantitative structure-activity relationships. J. Med. Chem. 2004, 47, 5541-5554.
21. Ghafourian, T.; Cronin, M.T. The impact of variable selection on the modelling of oestrogenicity. SAR QSAR Environ. Res. 2005, 16, 171-190.
22. Mackay, D.J.C. Probable networks and plausible predictions - a review of practical bayesian methods for supervised neural networks. Netw.-Comput. Neural. Syst. 1995, 6, 469-505.
23. Wang, Y.H.; Li, Y.; Yang, S.L.; Yang, L. An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network. J. Comput.-Aided Mol. Design 2005, 19, 137-147.
24. Vu, A.T.; Cohn, S.T.; Manas, E.S.; Harris, H.A.; Mewshaw, R.E. ER beta ligands. Part 4: Synthesis and structure-activity relationships of a series of 2-phenylquinoline derivatives. Bioorg. Med. Chem. Lett. 2005, 15, 4520-4525.
25. Wang, Y.H.; Li, Y.; Ding, J.; Wang, Y.; Chang, Y.Q. Prediction of binding affinity for estrogen receptor(alpha) modulators using statistical learning approaches. Mol. Divers. 2008, 12, 93-102.
26. Geladi, P.; Kowalski, B.R. Partial least-squares regression: A tutorial. Anal. Chim. Acta 1986, 185, 1-17.
27. Rosipal, R.; Kramer, N. Overview and recent advances in partial least squares. In Subspace, Latent Structure and Feature Selection; Saunders, C., Grobelnik, M., Gunn, S.R., Shawe-Taylor, J., Eds.; Springer: New York, NY, USA, 2006; pp. 34-51.
28. Caballero, J.; Fernandez, M. Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks. J. Mol. Model. 2006, 12, 168-181.
29. Crucianu, M.; Bone, R.; de Beauville, J.P.A. Bayesian learning for recurrent neural networks. Neuralocomputing 2001, 36, 235-242.
30. Mackay, D.J.C. Bayesian Interpolation. Neural Comput. 1992, 4, 415-447.
31. Foresee, F.D.; Hagan, M.T. Gauss-Newton approximation to Bayesian regularization. In Proceedings of the 1997 International Joint Conference on Neural Networks, Houston, TX, USA, 9-12 June, 1997.
32. Hagan, M.T.; Menhaj, M.B. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 1994, 5, 989-993.
33. Nguyen, D.; Widrow, B. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In 1990 IJCNN International Joint Conference on Neural Networks, Washington, DC, USA, 17-21 June, 1990.
34. Mackay, D.J.C. A practical bayesian framework for backpropagation networks. Neural Comput. 1992, 4, 448-472.
35. Kohonen, T.; Somervuo, P. Self-organizing maps of symbol strings. Neuralocomputing 1998, 21, 19-30.
36. Wang, Y.H.; Han, K.L.; Yang, S.L.; Yang, L. Structural determinants of steroids for cytochrome P450 3A4-mediated metabolism. J. Mol. Struct.-Theochem. 2004, 710, 215-221.
37. Jain, A.N. Surflex: Fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem. 2003, 46, 499-511.
38. Jain, A.N.; Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J. Comput.-Aided Mol. Design 2007, 21, 281-306.
39. Pike, A.C.; Brzozowski, A.M.; Hubbard, R.E.; Bonn, T.; Thorsell, A.G.; Engstrom, O.; Ljunggren, J.; Gustafsson, J.A.; Carlquist, M. Structure of the ligand-binding domain of oestrogen receptor beta in the presence of a partial agonist and a full antagonist. EMBO J. 1999, 18, 4608-4618.
40. Salum, L.B.; Polikarpov, I.; Andricopulo, A.D. Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity. J. Chem. Inf. Model. 2008, 48, 2243-2253.
41. Todeschini, R.; Consonni, Mannhold, R.; Kubinyi, H.; Timmerman, H. Handbook of Molecule Descriptors; Wiley-VCH: Weinheim, Germany, 2000.
42. Feher, M.; Williams, C.I. Effect of input differences on the results of docking calculations. J. Chem. Inf. Model. 2009, 49, 1704-1714.
43. Wolohan, P.; Reichert, D.E. CoMFA and docking study of novel estrogen receptor subtype selective ligands. J. Comput. Aided Mol. Des. 2003, 17, 313-328.
44. Mukherjee, S.; Nagar, S.; Mullick, S.; Mukherjee, A.; Saha, A. Pharmacophore mapping of selective binding affinity of estrogen modulators through classical and space modeling approaches: Exploration of bridged-cyclic compounds with diarylethylene linkage. J. Chem. Inf. Model. 2007, 47, 475-487.
45. Kekenes-Huskey, P.M.; Muegge, I.; von Rauch, M.; Gust, R.; Knapp, E.W. A molecular docking study of estrogenically active compounds with 1,2-diarylethane and 1,2-diarylethene pharmacophores. Bioorg. Med. Chem. 2004, 12, 6527-6537.
46. Liao, S.Y.; Qian, L.; Miao, T.F.; Lu, H.L.; Zheng, K.C. CoMFA and docking studies of 2-phenylindole derivatives with anticancer activity. Eur. J. Med. Chem. 2009, 44, 2822-2827.
47. Yang, J.M.; Chen, Y.F.; Shen, T.W.; Kristal, B.S.; Hsu, D.F. Consensus scoring criteria for improving enrichment in virtual screening. J. Chem. Inf. Model. 2005, 45, 1134-1146.