Cannabinoid CB2/CB1 selectivity. Receptor modeling and automated docking analysis.
ABSTRACT Three-dimensional models of the CB1 and CB2 cannabinoid receptors were constructed by means of a molecular modeling procedure, using the X-ray structure of bovine rhodopsin as the initial template, and taking into account the available site-directed mutagenesis data. The cannabinoid system was studied by means of docking techniques. An analysis of the interaction of WIN55212-2 with both receptors showed that CB2/CB1 selectivity is mainly determined by the interaction in the CB2 with the nonconserved residues S3.31 and F5.46, whose importance was suggested by site-directed mutagenesis data. We also carried out an automated docking of several ligands into the CB2 model, using the AUTODOCK 3.0 program; the good correlation obtained between the estimated free energy binding and the experimental binding data confirmed our binding hypothesis and the reliability of the model.
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ABSTRACT: The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA—principal component analysis and HCA—hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R 3 (charge density on substituent at position C3), Q 1 (charge on atom C1), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.Structural Chemistry 04/2012; 20(4):577-585. · 1.77 Impact Factor
Dataset: Latek CB JMM2011
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ABSTRACT: Because of the high conservation of ATP-binding sites in kinases, the quest for selective kinase inhibitors has been increasingly urgent in recent years. The Aurora kinase family represents attractive targets in cancer therapy and several small molecule inhibitors targeting Aurora kinases are undergoing clinical trials. Among them, MLN8054 has been proved to be a selective Aurora-A inhibitor, and is currently being evaluated in a phase I trial for patients with advanced solid tumors. But the detailed selectivity mechanism of MLN8054 towards Aurora-A over Aurora-B is still not resolved. In the present work, this selectivity mechanism was investigated using molecular dynamics simulations and binding free energy calculations. The predicted binding conformations and binding affinities of MLN8054 to Aurora-A and its mutant that mimics Aurora-B suggest that there exists stronger interaction between MLN8054 and Aurora-A through an induced DFG-up conformation. Further analyses can provide some information about the structural basis for the selectivity mechanism. Binding of MLN8054 to Aurora-A induces the conformation of the activation loop to adopt an unusual DFG-up conformation and opens the hydrophobic pocket of the active site, thus increasing the interaction between MLN8054 and the residue Val279. The residue Glu177 in Aurora-B displays electrostatic repulsion with MLN8054, while the corresponding Thr217 in Aurora-A has favorable interactions with MLN8054. The conformation change and the difference between the binding pockets for Aurora-A and B are key factors responsible for the selectivity. The results could be helpful for the rational design of selective inhibitors of Aurora-A kinase.Molecular BioSystems 09/2012; 8(11):3049-60. · 3.35 Impact Factor