Docking and three-dimensional quantitative structure-activity relationship (3D QSAR) analyses of nonsteroidal progesterone receptor ligands.

We report a docking and comparative molecular similarity indices analysis (CoMSIA) study of progesterone receptor (PR) ligands with an emphasis on nonsteroids including tanaproget. The ligand alignment generation, a critical part of model building, comprised two stages. First, thorough conformational sampling of docking poses within the PR binding pocket was made with the program GOLD. Second, a strategy to select representative poses for CoMSIA was developed utilizing the FlexX scoring function. After manual replacement of five poses where this approach had problems, a significant correlation (r(2) = 0.878) between the experimental affinities and electrostatic, hydrophobic, and hydrogen bond donor properties of the aligned ligands was found. Extensive model validation was made using random-group cross-validations, external test set predictions (r(pred)(2) = 0.833), and consistency check between the CoMSIA model and the PR binding site structure. Robustness, predictive ability, and automated alignment generation make the model a potential tool for virtual screening.

Three-dimensional structure-activity relationships of nonsteroidal ligands in complex with androgen receptor ligand-binding domain.

We studied the three-dimensional quantitative structure-activity relationships (3D QSAR) of 70 structurally and functionally diverse androgen receptor (AR) binding compounds using the comparative molecular similarity indices analysis (CoMSIA) method. The compound set contained 67 nonsteroidal analogues of flutamide, nilutamide, and bicalutamide whose binding mode to AR was unknown. Docking was used to identify the preferred binding modes for the nonsteroidal compounds within the AR ligand-binding pocket (LBP) and to generate the ligand alignment for the 3D QSAR analysis. The alignment produced a statistically significant and predictive model, validated by random group cross-validation and external test sets (q(2)(LOO) = 0.656, SDEP = 0.576, r(2) = 0.911, SEE = 0.293; q(2)(10) = 0.612, q(2)(5) = 0.571; pred-r(2) = 0.800). Additional model validation comes from the CoMSIA maps that were interpreted with respect to the LBP structure. The model takes into account and links the AR LBP structure, docked ligand structures, and the experimental binding activities. The results provide valuable information on intermolecular interactions between nonsteroidal ligands and the AR LBP.

Computationally identified novel diphenyl- and phenylpyridine androgen receptor antagonist structures.

We have identified and profiled a set of androgen receptor (AR) binding compounds representing two nonsteroidal scaffolds from a public chemical database supplied by Asinex with virtual screening procedure incorporating our recently published 3D QSAR model of AR ligands. The diphenyl- and phenylpyridine-based compounds act as antagonists in wild-type AR in CV1 cells and also retain this antagonistic character in CV1 cells expressing T877A mutant receptor. This mutation is frequently associated with prostate cancer. Two of the compounds repress the androgen-dependent cell growth of LNCaP prostate cancer cells expressing the T877A AR mutant. Molecular modeling of the observed in vitro antagonism with induced fit docking suggests that W741 and M895 could be mechanistically involved in the initiation of the antagonism. The results indicate finding of nonsteroidal AR antagonist compounds from a public chemical database with computational methods. Compounds could serve as a novel platform to develop more potent AR antagonists with inhibitory activity in both wild-type and T877A mutant AR.

SOMA--workflow for small molecule property calculations on a multiplatform computing grid.

We present the concept of the SOMA workflow developed at the Finnish IT Center for Science CSC. The SOMA workflow unites multiplatform UNIX/LINUX computing resources and third-party software for calculating molecular structure and properties. The presented workflow components consist of the computing program XML descriptions, the core workflow program Grape, the toolkit for parsing program input and output, and the extranet interface. The program Grape and the developed XML descriptions of scientific programs allow researchers to link molecular modeling software into highly sophisticated computational workflows. SOMA collects the calculated data produced by the workflow and stores the computed information in the Chemical Markup Language (CML) format. The extranet interface is used for user authentication, building of the program interfaces and the workflows, and for sorting, filtering, and visualizing the results.