This study presents for the first time the 3D model of the σ 1 receptor protein as obtained from homology modeling techniques, shows the applicability of this structure to docking-based virtual screening, defines a computational strategy to optimize the results based on a combination of 3D pharmacophore-based docking and MM/PBSA free energy of binding scoring, and provides evidence that these in silico models and recipes are powerful tools on which virtual screening of new σ 1 ligands can be based. In particular, the validation of the applicability of docking-based virtual screening to homology models is of utmost importance, since no crystal structure is available to date for the σ 1 receptor, and this missing information still constitutes a major hurdle for a rational ligand design for this important protein target.
Novel benzo [d ]oxazol-2(3H )-one derivatives were designed and synthesized, and their affinities against σ receptors were evaluated. On the basis of 31 compounds, a three-dimensional pharmacophore model for the σ 1 receptor binding site was developed using the Catalyst 4.9 software package. The best 3D pharmacophore hypothesis, consisting of one positive ionizable, one hydrogen bond acceptor, two hydrophobic aromatic, and one hydrophobic features provided a 3D-QSAR model with a correlation coefficient of 0.89. The best hypothesis was also validated by three independent methods, i.e., the Fisher randomization test included in the CatScramble functionality of Catalyst, the leave-one-out test, and activity prediction of an additional test set. The achieved results will allow researchers to use this 3D pharmacophore model for the design and synthesis of a second generation of high affinity σ 1 ligands, as well as to discover other lead compounds for this class of receptors.
The combined experimental and computational approach can be helpful in targeted drug design, thus yielding valuable information for the synthesis and prediction of activity of a second generation of inhibitors.
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