Mechanistic Insights into the Allosteric Inhibition of Androgen Receptors by Binding Function 3 Antagonists from an Integrated Molecular Modeling Study

Characterization of allosteric modulators that disrupt androgen receptor co-activator protein-protein interactions to alter transactivation-Drug leads for metastatic castration resistant prostate cancer

Three series of compounds were prioritized from a high content screening campaign that identified molecules that blocked dihydrotestosterone (DHT) induced formation of Androgen Receptor (AR) protein-protein interactions (PPIs) with the Transcriptional Intermediary Factor 2 (TIF2) coactivator and also disrupted preformed AR-TIF2 PPI complexes; the hydrobenzo-oxazepins (S1), thiadiazol-5-piperidine-carboxamides (S2), and phenyl-methyl-indoles (S3). Compounds from these series inhibited AR PPIs with TIF2 and SRC-1, another p160 coactivator, in mammalian 2-hybrid assays and blocked transcriptional activation in reporter assays driven by full length AR or AR-V7 splice variants. Compounds inhibited the growth of five prostate cancer cell lines, with many exhibiting differential cytotoxicity towards AR positive cell lines. Representative compounds from the 3 series substantially reduced both endogenous and DHT-enhanced expression and secretion of the prostate specific antigen (PSA) cancer biomarker in the C4-2 castration resistant prostate cancer (CRPC) cell line. The comparatively weak activities of series compounds in the H3-DHT and/or TIF2 box 3 LXXLL-peptide binding assays to the recombinant ligand binding domain of AR suggest that direct antagonism at the orthosteric ligand binding site or AF-2 surface respectively are unlikely mechanisms of action. Cellular enhanced thermal stability assays (CETSA) indicated that compounds engaged AR and reduced the maximum efficacy and right shifted the EC50 of DHT-enhanced AR thermal stabilization consistent with the effects of negative allosteric modulators. Molecular docking of potent representative hits from each series to AR structures suggest that S1-1 and S2-6 engage a novel binding pocket (BP-1) adjacent to the orthosteric ligand binding site, while S3-11 occupies the AR binding function 3 (BF-3) allosteric pocket. Hit binding poses indicate spaces and residues adjacent to the BP-1 and BF-3 pockets that will be exploited in future medicinal chemistry optimization studies. Small molecule allosteric modulators that prevent/disrupt AR PPIs with coactivators like TIF2 to alter transcriptional activation in the presence of orthosteric agonists might evade the resistance mechanisms to existing prostate cancer drugs and provide novel starting points for medicinal chemistry lead optimization and future development into therapies for metastatic CRPC.

Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning

The mechanism of transcriptional activation/repression of the nuclear receptors (NRs) involves two main conformations of the NR protein, namely, the active (agonistic) and inactive (antagonistic) conformations. Binding of agonists or antagonists to the ligand-binding pocket (LBP) of NRs can regulate the downstream signaling pathways with different physiological effects. However, it is still hard to determine the molecular type of a LBP-bound ligand because both the agonists and antagonists bind to the same position of the protein. Therefore, it is necessary to develop precise and efficient methods to facilitate the discrimination of agonists and antagonists targeting the LBP of NRs. Here, combining structural and energetic analyses with machine-learning (ML) algorithms, we constructed a series of structure-based ML models to determine the molecular category of the LBP-bound ligands. We show that the proposed models work robustly and with high accuracy (ACC > 0.9) for determining the category of molecules derived from docking-based and crystallized poses. Furthermore, the models are also capable of determining the molecular category of ligands with dual opposite functions on different NRs (i.e., working as an agonist in one NR target, whereas functioning as an antagonist in another) with reasonable accuracy. The proposed method is expected to facilitate the determination of the molecular properties of ligands targeting the LBP of NRs with structural interpretation.

Molecular View on the Impact of DHA Molecules on the Physical Properties of a Model Cell Membrane

Docosahexaenoic acid (DHA) is a ω-3 polyunsaturated fatty acid, which can be uptaken by cells and is essential for proper neuronal and retinal function. However, the detailed physical impact of DHA molecules on the plasma membrane is still unclear. Hence, in this work, we carried out μs-scale coarse-grained molecular dynamics (MD) simulations to reveal the interactions between DHA molecules and a model cell membrane. As is known, the cell membrane can segregate into liquid-ordered (L_o) and liquid-disordered (L_d) membrane domains due to the differential interactions between lipids and proteins. In order to capture this feature, we adopted the three-component phase-separated lipid membranes and considered both anionic and neutral DHA molecules in the current work. Our results showed that DHA molecules can spontaneously self-assemble into nanoclusters, fuse with lipid membranes, and localize preferably in L_d membrane domains. During the membrane fusion process, DHA molecules can change the intrinsic transmembrane potential of the lipid membrane, and the effects of anionic DHA molecules are much more significant. Besides, the presence of DHA molecules mainly in the L_d membrane domains could regulate the differences in the lipid chain order, membrane thickness, cholesterol preference, and cholesterol flip-flop basically in a concentration-dependent manner, which further promote the stability of the intraleaflet dynamics and inhibit the interleaflet dynamics (or promote membrane domain registration) of the membrane domains. In short, the impact of DHA molecules on the physical properties of a model cell membrane on the molecular level revealed in our work will provide useful insights for understanding the biological functions of DHA molecules.