Computational and functional analysis of the androgen receptor antagonist atraric acid and its derivatives.

Androgen receptor (AR) antagonists are important compounds for the treatment of prostate cancer (PCa). The atraric acid (AA), a natural compound, binds to the AR and acts as a specific AR antagonist. Interestingly, AA represents a novel chemical platform that could serve as a potential basis for new AR antagonists. Therefore, one objective of this study was to analyze the chemical/structural requirements for AR antagonism and to obtain predictions of where and how AA binds to the AR. Further, this study describes the chemical synthesis of 12 AA derivatives and their analysis using a combination of computational and functional assays. Functional analysis of AA derivatives indicated that none activated the AR. Both the para-hydroxyl group and the benzene ortho- and the meta-methyl groups of AA appeared to be essential to antagonize androgen-activated AR activity. Furthermore, extension of the hydrophobic side chain of AA led to slightly stronger AR antagonism. In silico data suggest that modifications to the basic AA structure change the hydrogen-bonding network with the AR ligand binding domain (LBD), so that the para-hydroxyl group of AA forms a hydrogen bond with the LBD, confirming the functional importance of this group for AR antagonism. Moreover, in silico modeling also suggested that the ortho- and meta- methyl groups of AA interact with hydrophobic residues of the ligand pocket of AR, which might explain their functional importance for antagonism. Thus, these studies identify the chemical groups of AA that play key roles in allowing the AA-based chemical platform to act as an AR antagonist.

Structural basis for computational screening of non-steroidal androgen receptor ligands.

IMPORTANCE OF THE FIELD: Deep structural and chemical understanding of the protein target and computational methods for detection of receptor-selective ligands are important for the early drug discovery in the steroid receptor field. AREAS COVERED IN THIS REVIEW: This review focuses on the use of currently available structural information of the androgen receptor (AR) and known AR ligands to make computational strategies for the discovery of AR ligands in order to offer new chemical platforms for drug development. WHAT THE READER WILL GAIN: AR is a challenging target for drug discovery and modeling even if there is a wealth of experimental data available. First, only the active structure of AR is currently known, which hampers the design of AR antagonists. Second, the structural similarity between the ligand-binding sites of AR and its mutated forms and closely related steroid receptors (SRs) such as progesterone receptors presents challenges for the development of drugs with receptor-selective function. TAKE HOME MESSAGE: Research indicates that a very small chemical change in the structure of a non-steroidal ligand can cause a complete change in its activity. One source of this effect arises from binding to similar binding sites in related SRs and other proteins in the signaling pathway. Currently, computational methods are not able to predict the subtle differences between AR ligand activities but modeling does offer the possibility of generating new lead structures that might have the desired properties.

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.

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.