Biased allosteric regulation of the Prostaglandin F2α receptor: from small molecules to large receptor complexes
Bibliographic record
Abstract
G protein-coupled receptors (GPCRs) represent the largest family of cell surface receptors, and thus some of the most important targets for drug discovery. By binding to the orthosteric site where endogenous ligands bind, agonists and antagonists differentially modulate signals sent downstream from these receptors. New evidence suggests that GPCRs possess topographically distinct or allosteric binding sites, which may differentially modulate agonist- and antagonist-mediated responses to selectively affect distinct signalling pathways coupled to the same receptor. These sites may either positively or negatively regulate receptor activity, depending on the pathway in question, and thus can act as biased ligands, leading to functional selectivity (or ligand-directed signalling). Another way of allosterically regulating GPCR signalling is through receptor oligomerization, which has recently emerged as a common mechanism for regulating receptor function. The GPCR for prostaglandin F2α FP, is implicated in many important physiological responses, such as parturition, smooth muscle cell contraction and blood pressure regulation. Therefore, evaluating the potential use of allosteric modulators of FP to fine-tune PGF2α-mediated signals, as well as generating a better understanding of its putative oligomerization partners would be of significant pharmacological and clinical interest. In this thesis, I studied the impact of modulating, in both heterologous (HEK 293 cells) and homologous (osteoblast, myometrial or vascular smooth muscle cells) systems, downstream cellular responses of FP by 1) an orthosteric, but biased ligand, previously characterized as a neutral antagonist 2) an allosteric molecule, designed based on the extracellular domains of FP, which had biased signalling properties and, 3) heterodimerization with a receptor partner, the angiotensin II type I receptor, where I demonstrated the asymmetrical organization of this new signalling unit both in vitro and in vivo. Overall, my thesis unveils important roles for biased, allosteric ligands and receptor oligomerization in modulating FP signalling. This work also demonstrates the importance of understanding distinct receptor conformations, and their effects on cellular responses, which are adopted when GPCRs are allosterically modulated, to design better therapeutics with improved efficacy profiles and reduced side effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".