Protein-Protein Interaction and Not Glycosylation Determines the Binding Selectivity of Heterodimers between the Calcitonin Receptor-like Receptor and the Receptor Activity-modifying Proteins
Bibliographic record
Abstract
The receptor activity-modifying proteins (RAMPs) and the calcitonin receptor-like receptor (CRLR) are both required to generate adrenomedullin (AM) and calcitonin gene-related peptide (CGRP) receptors. A mature, fully glycosylated, form of CRLR was associated with (125)I-CGRP binding, upon co-expression of RAMP1 and CRLR. In contrast, RAMP2 and -3 promoted the expression of smaller, core-glycosylated, CRLR forms, which were linked to AM receptor pharmacology. Since core glycosylation is classically a trademark of immature proteins, we tested the hypothesis that the core-glycosylated CRLR forms the AM receptor. Although significant amounts of core-glycosylated CRLR were produced upon co-expression with RAMP2 or -3, cross-linking experiments revealed that (125)I-AM only bound to the fully glycosylated forms. Similarly, (125)I-CGRP selectively recognized the mature CRLR species upon co-expression with RAMP1, indicating that the glycosylation does not determine ligand-binding selectivity. Our results also show that the three RAMPs lie close to the peptide binding pocket within the CRLR-RAMP heterodimers, since (125)I-AM and (125)I-CGRP were incorporated in RAMP2, -3, and -1, respectively. Cross-linking also stabilized the peptide-CRLR-RAMP ternary complexes, with the expected ligand selectivity, indicating that the fully processed heterodimers represent the functional receptors. Overall, the data indicate that direct protein-protein interactions dictate the pharmacological properties of the CRLR-RAMP complexes.
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) | 0.000 | 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 | 0.000 |
| 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".