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4.6.3. Receptor setup
The homology model of 5-HT1AR was setup for docking as fol-
lows: receptor was prepared by protein preparation wizard proto-
col implemented in Maestro as described in the homology
modeling section, and Kollman united-atom partial charges were
as-signed. The ADDSOL utility of AutoDock was used to add sol-
vation parameters to the protein structures, and the grid maps
representing the proteins in the docking process were calculated
using AutoGrid. The grids, one for each atom type in the inhibitor
plus one for the electrostatic interactions, were chosen to be large
enough to include not only the hypothetical channel binding site
sites but also a significant part of the protein around it. As a con-
sequence, the dimensions of the grid maps were 100 ꢁ 100 ꢁ 100
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ꢀ
points, with a grid-point spacing of 0.375 A.
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4.6.4. Docking calculations
All the docked compounds were subjected to 256 independent
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using the Lamarckian genetic algorithm and through a protocol
with an initial population of 300 randomly placed individuals, a
maximum number of 50 million energy evaluations, a mutation
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F. Carnovali, L. Cervo, T. Mennini, Synthesis and pharmacological evaluation of
potent and highly selective D3 receptor ligands: inhibition of cocaine-seeking
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3822e3839;
(d) S. Butini, S. Gemma, G. Campiani, S. Franceschini, F. Trotta, M. Borriello,
N. Ceres, S. Ros, S.S. Coccone, M. Bernetti, M. De Angelis, M. Brindisi, V. Nacci,
I. Fiorini, E. Novellino, A. Cagnotto, T. Mennini, K. Sandager-Nielsen,
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and effects on behavior, J. Med. Chem. 52 (2009) 151e169.
ꢀ
formations that differed by less than 2.0 A in positional RMSD were
clustered together. Cluster analysis was performed by selecting the
lowest energy solution of the most populated cluster. The lowest
energy solutions resulting from the docking calculations in each
case belong to the most populated cluster; the most populated
clusters were taken into account for their consistency with exper-
imental data. All estimated binding energies was in agreement with
the nanomolar range of the biological data. All protonated con-
formations were optimized and the point charges were obtained by
means of ab-initio calculations, and then used into the docking
simulations.
[16] A. Cappelli, M. Anzini, S. Vomero, L. Mennuni, F. Makovec, E. Doucet,
M. Hamon, M.C. Menziani, P.G. De Benedetti, G. Giorgi, C. Ghelardini,
S. Collina, Novel potent 5-HT3 receptor ligands based on the pyrrolidone
structure: synthesis, biological evaluation, and computational rationalization
of the ligand-receptor interaction modalities, Bioorg. Med. Chem. 10 (2002)
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piperazin-1-yl)alkyl)carboxamides as dopamine D2 and D3receptor ligands,
J. Med. Chem. 46 (2003) 3883e3899;
Acknowledgments
The authors are grateful to Dr. Francesco Berrettini (CIADS,
Università di Siena) for the X-ray data collection. This work was
partly supported by MIUR (Ministero dell’Istruzione, dell’Università
e della Ricerca) - PRIN (Programmi di ricerca di Rilevante Interesse
Nazionale).
(b) M.J. Robarge, S.M. Husbands, A. Kieltyka, R. Brodbeck, A. Thurkauf,
A.H. Newman, Design and synthesis of [(2,3-dichlorophenyl)piperazin-1-yl]
alkylfluorenylcarboxamides as novel ligands selective for the dopamine D3
receptor subtype, J. Med. Chem. 44 (2001) 3175e3186;
(c) M. Resimont, J.F. Liegeois, Synthesis and in vitro binding studies of
piperazine-alkyl-naphthamides: impact of homology and sulphonamide/car-
boxamide bioisosteric replacement on the affinity for 5-HT1A, alpha2A, D4.2,
D3 and D2L receptors, Bioorg. Med. Chem. Lett. 20 (2010) 5199e5202.
[18] M.D. Hall, S. el Mestikawy, M.B. Emerit, L. Pichat, M. Hamon, H. Gozlan, [3H]8-
hydroxy-2-(di-n-propylamino)tetralin binding to pre- and postsynaptic 5-
hydroxytryptamine sites in various regions of the rat brain, J. Neurochem.
44 (1985) 1685e1696.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
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A.J. Olson, Automated docking using a Lamarckian genetic algorithm and an
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