A single domain intrabody as a novel tool to bias the subcellular trafficking of the follicle-stimulating hormone receptor
Bibliographic record
Abstract
Intracellular variable fragments from heavy-chain only antibodies of camelids (intra-VHH) have been successfully used for their stabilizing properties to solve the 3D structure of active G protein-coupled receptors (GPCRs) bound to their cognate transducers. They also provide tools to link a given conformation of a GPCR to the signalling network engaged, thus allowing extensive structure/activity studies. Recently, they have been instrumental in tracking active GPCRs in various subcellular compartments. Here, we report the isolation and characterization of iPRC2, an intra-VHH recognizing the 1st and 3rd intracellular loops of the FSHR, but not of the luteinizing hormone/choriogonadotropin receptor close relative. Its expression in the cell decreases the cAMP production in response to hormone binding, and requires G𝛼s for optimal interaction with the receptor. Importantly, iPRC2 increases the FSHR accumulation in the early endosomes, and consequently, diminishes its recycling to the cell surface. Hence, in contrast to previously described intra-VHH that disclose active GPCR intracellular location, iPRC2 provokes per se a location bias, through its ability to reroute the FSHR. Thus, it is an innovative tool to examine the functional consequences of GPCR accumulation in various sub-cellular compartments.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".