There is an increasing body of evidence supporting the involvement of alpha-7 nicotinic acetylcholine receptors (nAChRs) in the pathophysiology of schizophrenia. As a consequence, a large number of diverse agonists and partial agonists have been synthesized. The selective alpha-7 agonists are usually characterized by a larger molecular volume compared to alpha-4-beta-2 agonists derived from nicotine. The prediction of their bioactive pose in the acetylcholine binding protein and in homology models of the alpha-7 receptor by molecular docking is not trivial, due to the existence of multiple distinct binding modes. We have recently described how pharmacophore-based modeling can be complemented with homology modeling to discover new nicotinic ligands and rationalize their biological activity profile. Here we report a newly synthesized series of 55 diazabicyclo[3.2.2]nonanes characterized as partial agonists at the alpha-7 nicotinic receptor subtype. We describe an innovative 3D-QSAR approach aimed at deriving the bioactive pose of the ligands taking into account only their conformational preferences and their biological activities, with no prior assumptions about pharmacophoric features and binding modes. Briefly, a conformational search was carried out on the whole dataset modeling solvent effects implicitly according to the Generalized Born approximation. After splitting the dataset into a training set and a test set, all stable conformers of compounds belonging to the training set were used as alignment templates for the rest of the dataset, from which the best fitting conformer of each compound was selected. The best-scoring alignment among those generated by Open3DALIGN was used to build a 3D-QSAR model with Open3DQSAR. This model gave useful hints about the requirements for effective binding at the alpha-7 receptor from the ligand’s perspective. The feasibility of the hypothesized alignment was subsequently challenged on structure-based grounds against an alpha-7 receptor homology model.
Probing the alpha-7 nicotinic receptor with selective ligands: a brute force 3D-QSAR approach.
TOSCO, Paolo;
2011-01-01
Abstract
There is an increasing body of evidence supporting the involvement of alpha-7 nicotinic acetylcholine receptors (nAChRs) in the pathophysiology of schizophrenia. As a consequence, a large number of diverse agonists and partial agonists have been synthesized. The selective alpha-7 agonists are usually characterized by a larger molecular volume compared to alpha-4-beta-2 agonists derived from nicotine. The prediction of their bioactive pose in the acetylcholine binding protein and in homology models of the alpha-7 receptor by molecular docking is not trivial, due to the existence of multiple distinct binding modes. We have recently described how pharmacophore-based modeling can be complemented with homology modeling to discover new nicotinic ligands and rationalize their biological activity profile. Here we report a newly synthesized series of 55 diazabicyclo[3.2.2]nonanes characterized as partial agonists at the alpha-7 nicotinic receptor subtype. We describe an innovative 3D-QSAR approach aimed at deriving the bioactive pose of the ligands taking into account only their conformational preferences and their biological activities, with no prior assumptions about pharmacophoric features and binding modes. Briefly, a conformational search was carried out on the whole dataset modeling solvent effects implicitly according to the Generalized Born approximation. After splitting the dataset into a training set and a test set, all stable conformers of compounds belonging to the training set were used as alignment templates for the rest of the dataset, from which the best fitting conformer of each compound was selected. The best-scoring alignment among those generated by Open3DALIGN was used to build a 3D-QSAR model with Open3DQSAR. This model gave useful hints about the requirements for effective binding at the alpha-7 receptor from the ligand’s perspective. The feasibility of the hypothesized alignment was subsequently challenged on structure-based grounds against an alpha-7 receptor homology model.
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