Macromolecular Modelling and Docking Simulations for the Discovery of Selective GPER Ligands

Abstract

Abstract. Estrogens influence multiple physiological processes and are implicated in many diseases as well. Cellular responses to estrogens are mainly mediated by the estrogen receptors (ER)α and ERβ, which act as ligand-activated transcription factors. Recently, a member of the G proteincoupled receptor (GPCR) superfamily, namely GPER/GPR30, has been identified as a further mediator of estrogen signalling in different pathophysiological conditions, including cancer. Today, computational methods are commonly used in all areas of health science research. Among these methods, virtual ligand screening has become an established technique for hit discovery and optimization. The absence of an established three-dimensional structure of GPER promoted studies of structure-based drug design in order to build reliable molecular models of this receptor. Here, we discuss the results obtained through the structure-based virtual ligand screening for GPER, which allowed the identification and synthesis of different selective agonist and antagonist moieties. These compounds led significant advances in our understanding of the GPER function at the cellular, tissue, and organismal levels. In particular, selective GPER ligands were critical toward the evaluation of the role elicited by this receptor in several pathophysiological conditions, including cancer. Considering that structure-based approaches are fundamental in drug discovery, future research breakthroughs with the aid of computer-aided molecular design and chemobioinformatics could generate a new class of drugs that, acting through GPER, would be useful in a variety of diseases as well as in innovative anticancer strategies.

A novel GPER antagonist protects against the formation of estrogen-induced cholesterol gallstones in female mice

“…The selectivity studies show that CIMBA and 21 M. While CIMBA 21 displays binding to M. These results are not surprising because the molecular modeling studies on GPER and the classical ER from our group and others are in agreement that estrogenic compounds bind within a similar binding pocket as each other (33,34,(44)(45)(46)(47). Due to the proposed similarity between the binding pockets for these three receptors, it is not surprising to observe off-target 24 binding by some GPER ligands at high concentrations (16)(17)(18).…”

Interaction of the Anti-Proliferative GPER Inverse Agonist ERα17p with the Breast Cancer Cell Plasma Membrane: From Biophysics to Biology

“…We have shown that the heptatransmembrane G protein-coupled estrogen receptor (GPER) was functionally and physically targeted by the peptide, which triggers an inverse agonist action [17,19,20]. Following a modeling approach, the N-terminal PLMI motif of the peptide ERα17p seems to engulf within a cavity located in the extracellular part of the GPER [17], where other ligands such as the quinoleins G-1 and G-15 and the benzopyrroloxazine PBX-2 interact [21][22][23]. Its mechanism of action refers to a pharmacological process through which it induces a proteasome-dependent decrease of GPER protein levels and abrogates the activation of (i) the epidermal growth factor receptor (EGFR), (ii) the extracellular signal-regulated kinase ERK1/2, and (iii) the transcription factor c-fos [17].…”

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

“…Both components have been the subject of intensive research and development in both academic and commercial settings. [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] Nonetheless, computational methods for pose prediction and affinity ranking have yet to fulfill their perceived promise, as neither is yet fully reliable. [45][46][47][48][49][50][51] In fact, it is surprisingly difficult even to compare the reliability of various methods in a consistent manner, and this limitation makes it correspondingly difficult to make and verify technical progress.…”