[Molecular modeling of interaction of 17(20)Z- and 17(20)E-pregna-5,17(20)-dien-21-oyl amides with the nuclear receptor LXRbeta].

Aiming the search of novel regulators of lipid metabolism and their potential targets, in this study we performed molecular modeling of eight isomeric 17(20)Z- and 17(20)E-pregna-5,17(20)-dien-21-oyl amides differing in structure of the amide moiety. Analysis of the low energy conformers revealed that all 17(20)E-isomers had three main energy minima (corresponding to values of the dihedral angle theta20,21 (C17 = C20-C21 = 0) to approximately 0 degrees, to approximately 120 degrees and to approximately 240 degrees), the most occupied minimum was found to correspond to theta20,21 to approximately 0 degrees; while 17(20)Z-isomers had either one or two pools of low energy conformations. Molecular docking of these compounds to the ligand-binding site of the nuclear receptor LXRbeta (a potential target) indicates high probability of binding for E-isomers and the absence of that for Z-isomers. Results of the molecular modeling were confirmed by an experiment in which stimulation of triglyceride biosynthesis in Hep G2 cells in the presence of 17(20)E-3beta-hydroxypregna-5,17(20)-dien-21-oyl (hydroxyethyl)amide was demonstrated.

[Molecular docking and 3D-QSAR on 16alpha,17alpha-cycloalkanoprogesterone analogues as progesterone receptor ligands].

A series of 42 steroid ligands was used to predict a binding affinity to progesterone receptor. The molecules were the derivatives of 16alpha,17alpha-cycloalkanoprogesterones. Different methods of prediction were used and analyzed such as CoMFA and artificial neural networks. The best result (Q2 = 0.91) was obtained for a combination of molecular docking, molecular dynamics simulation and artificial neural networks. A predictive power of the model was validated by a group of 8 pentarans synthesized separately and tested in vitro (R2test = 0.77). This model can be used to determine the affinity level of the ligand to progesterone receptor and accurate ranking of binding compounds.

[Mode of action prediction of ligands of steroid hormone receptors].

The several predictive models based on two well-known methods PASS and SIMCA were created. These models predict a type of physiological response of steroid compounds binding to nuclear receptors of steroid hormones. We considered 10 variants: the agonists and the antagonists of estrogen, progesterone, androgen, glucocorticoid and mineralocorticoid receptors respectively. Two different sets of descriptors were used during SIMCA (the Dragon descriptors and indices of similarity). The results of discriminant analysis are good enough with average accuracy of 80-85%.