Functional Selective Oxytocin-derived Agonists Discriminate between Individual G Protein Family Subtypes
Bibliographic record
Abstract
We used a bioluminescence resonance energy transfer biosensor to screen for functional selective ligands of the human oxytocin (OT) receptor. We demonstrated that OT promoted the direct engagement and activation of G(q) and all the G(i/o) subtypes at the OT receptor. Other peptidic analogues, chosen because of specific substitutions in key OT structural/functional residues, all showed biased activation of G protein subtypes. No ligand, except OT, activated G(oA) or G(oB), and, with only one exception, all of the peptides that activated G(q) also activated G(i2) and G(i3) but not G(i1), G(oA), or G(oB), indicating a strong bias toward these subunits. Two peptides (DNalOVT and atosiban) activated only G(i1) or G(i3), failed to recruit β-arrestins, and did not induce receptor internalization, providing the first clear examples of ligands differentiating individual G(i/o) family members. Both analogs inhibited cell proliferation, showing that a single G(i) subtype-mediated pathway is sufficient to prompt this physiological response. These analogs represent unique tools for examining the contribution of G(i/o) members in complex biological responses and open the way to the development of drugs with peculiar selectivity profiles. This is of particular relevance because OT has been shown to improve symptoms in neurodevelopmental and psychiatric disorders characterized by abnormal social behaviors, such as autism. Functional selective ligands, activating a specific G protein signaling pathway, may possess a higher efficacy and specificity on OT-based therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".