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Molecular Diversity

, Volume 11, Issue 3–4, pp 171–181 | Cite as

Structural features of diverse ligands influencing binding affinities to estrogen α and estrogen β receptors. Part II. Molecular descriptors calculated from conformation of the ligands in the complex resulting from previous docking study

  • Morena Spreafico
  • Elena Boriani
  • Emilio Benfenati
  • Marjana Novič
Full Length Paper

Abstract

A QSAR study is reported, in which the relationship between chemical structure of a set of compounds and the binding affinity to human estrogen receptor α and β (ER-α and ER-β) is modelled. Counterpropagation neural networks are used to predict experimental binding affinity of a range of substances. Several compounds as estrogenic chemicals, phytoestrogens, and natural and synthetic estrogens are treated with a structure-based approach that involves the protein structure. The conformations obtained with a docking methodology are used to calculate molecular descriptors. The models are built up with the neural network training procedure, which encodes the information present in molecular descriptors and related binding affinities of the pre-selected training set of compounds. In order to reach the best possible models, a selection of the descriptors using genetic algorithm was conducted. The selection was directed by the error in the prediction of binding affinities of compounds from the test set. The final models obtained for estrogen receptor α and β were tested with an external validation set and were compared with the models obtained from a receptor-independent approach reported in the accompanying paper.

Keywords

Estrogen receptor α Estrogen receptor β Counterpropagation artificial neural network Conformational analysis Docking Genetic algorithm Structural features Selection of descriptors 

Abbreviations

CP-ANN

Counterpropagation artificial neuronal network

ER

Estrogen receptor

GA

Genetic algorithm

RBA

Relative binding affinity

RMS

Root mean square error

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References

  1. 1.
    Kuiper GJM, Carlsson B, Grandien K, Enmark E, Haggblad J, Nilsson S, Gustafsson JA (1997) Comparison of the ligand binding specificity and transcript tissue distribution of estrogen receptor α and β. Endocrinology 138: 863–870CrossRefGoogle Scholar
  2. 2.
    Brzozowski AM, Pike ACW, Dauter Z, Hubbard RE, Bonn T, Engstrom O, Ohman L, Greene GL, Gustafsson JA, Carlquist M (1997) Molecular basis of agonism and antagonism in the oestrogen receptor. Nature 389: 753–758CrossRefGoogle Scholar
  3. 3.
    Kuiper GGJM, Lemmen JG, Carlsson B, Corton JC, Safe SH, Saag PT, Burg P, Gustafsson JA (1998) Interaction of estrogenic chemicals and phytoestrogens with estrogen receptor β. Endocrinology 139: 4252–4263CrossRefGoogle Scholar
  4. 4.
    Henke BR, Consler TG, Go N, Hale RL, Hohman DR, Jones SA, Lu AT, Moore LB, Moore JT, Orband-Miller LA, Robinett RG, Shearin J, Spearing PK, Stewart EL, Turnbull PS, Weaver SL, Williams SP, Wisely GB, Lambert MH (2002) A new series of estrogen receptor modulators that display selectivity for estrogen receptor beta. J Med Chem 45: 5492–5505CrossRefGoogle Scholar
  5. 5.
    Kim S, Wu JY, Birzin ET, Frisch K, Chan W, Pai LY, Yang YT, Mosley RT, Fitzgerald PM, Sharma N, Dahllund J, Thorsell AG, DiNinno F, Rohrer SP, Schaeffer JM, Hammond ML (2004) Estrogen receptor ligands. II. Discovery of benzoxathiins as potent, selective estrogen receptor alpha modulators. J Med Chem 47: 2171–2175CrossRefGoogle Scholar
  6. 6.
    Leach AR, Shoichet BK, Peishoff CE (2006) Docking and scoring. J Med Chem 49: 5851–5855CrossRefGoogle Scholar
  7. 7.
    Shoichet BK, McGovern SL, Wei B, Irwin JJ (2002) Lead discovery using molecular docking. Curr Opin Chem Biol 6: 439–446CrossRefGoogle Scholar
  8. 8.
    Hecht-Nielsen R (1987) Counterpropagation networks. Appl Optics 26: 4979–4984CrossRefGoogle Scholar
  9. 9.
    Zupan J, Gasteiger J (1999) Neural networks in chemistry and drug design, 2nd edn. Wiley-VCH, WeinheimGoogle Scholar
  10. 10.
    Davis, L (eds) (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New YorkGoogle Scholar
  11. 11.
    Hibbert DB (1993) Genetic Algorithms in chemistry. Chemometr Intell Lab 19: 277–293CrossRefGoogle Scholar
  12. 12.
    Katritzky AR, Lobanov VS, Karelson M (1994) Comprehensive descriptors for structural and statistical analysis reference manual, Version 2.0. University of FloridaGoogle Scholar
  13. 13.
    Venkatachalam CM, Jiang X, Oldfield T, Waldman M (2003) LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J Mol Graph Model 21: 289–307CrossRefGoogle Scholar
  14. 14.
    Boriani E, Spreafico M, Benfenati E, Novič M (2008) Structural features of diverse ligands influencing binding affinities to estrogen α and estrogen β receptors. Part I: molecular descriptors calculated from minimal energy conformation of isolated ligands. Mol Divers. doi: 10.1007/s11030-008-9069-9
  15. 15.
    Stewart JJP (1989) Optimization of parameters for semi-empirical methods. I—method. J Comp Chem 10:209–220; (b) Stewart JJP (1989) Optimization of parameters for semi-empirical methods II—applications. J Comput Chem 10:221–264Google Scholar
  16. 16.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242. http://www.pdb.org/ Google Scholar
  17. 17.
    Walters P, Stahl M (1992–1996) Babel version 1.3. University of ArizonaGoogle Scholar
  18. 18.
    Kohonen T (1998) Self-organization and associative memory. Springer-Verlag, BerlinGoogle Scholar
  19. 19.
    Harris HA, Bapat AR, Gonder DS, Frail DE (2002) The ligand binding profiles of estrogen receptors α and β are species dependent. Steroids 67: 379–384CrossRefGoogle Scholar
  20. 20.
    Roncaglioni A, Spreafico M, Boriani E, Benfenati E, Novič M (2004) In silico tools for the screening of oestrogen receptor binding affinity, CASCADE Summer School on Nuclear Hormone Receptors. 13th–17th September 2004, Ecole Normale Supérieure de Lyon, LyonGoogle Scholar
  21. 21.
    Zefirov NS, Kirpichenok MA, Izmailov FF, Trofimov MI (1987) Calculation schemes for atomic electronegativities in molecular graphs within the framework of Sanderson principle. Dokl Akad Nauk SSSR 296: 883–887Google Scholar
  22. 22.
    Kirpichenok MA, Zefirov NS (1987) Electronegativity and molecular geometry. I. General principles of the method and analysis of the effect of short-range electrostatic interactions on bond lengths in organic molecules. Zh Org Khim 23: 673–691Google Scholar
  23. 23.
    KOW WIN v1.66, on-line demo, available from: http://www.syrres.com/esc/kowwin.htm, http://www.logp.com/
  24. 24.
    Oostenbrink BC, Pitera JW, van Lipzig MMH, Meerman JHN, Gunsteren WF (2000) Simulations of the estrogen receptor ligand-binding domain: affinity of natural ligands and xenoestrogens. J Med Chem 43: 4594–4605CrossRefGoogle Scholar
  25. 25.
    Oostenbrink BC, van Gunsteren WF (2004) Free energies of binding of polychlorinated biphenyls to the estrogen receptor from a single simulation. Proteins 54: 237–246CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Morena Spreafico
    • 1
    • 2
  • Elena Boriani
    • 1
    • 2
  • Emilio Benfenati
    • 2
  • Marjana Novič
    • 1
  1. 1.Laboratory for ChemometricsNational Institute of ChemistryLjubljanaSlovenia
  2. 2.Mario Negri Institute for Pharmacological ResearchMilanItaly

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