Pepducin targeting the C-X-C chemokine receptor type 4 acts as a biased agonist favoring activation of the inhibitory G protein
Bibliographic record
Abstract
Short lipidated peptide sequences derived from various intracellular loop regions of G protein-coupled receptors (GPCRs) are named pepducins and act as allosteric modulators of a number of GPCRs. Recently, a pepducin selectively targeting the C-X-C chemokine receptor type 4 (CXCR4) was found to be an allosteric agonist, active in both cell-based assays and in vivo. However, the precise mechanism of action of this class of ligands remains poorly understood. In particular, given the diversity of signaling effectors that can be engaged by a given receptor, it is not clear whether pepducins can show biased signaling leading to functional selectivity. To explore the ligand-biased potential of pepducins, we assessed the effect of the CXCR4 selective pepducin, ATI-2341, on the ability of the receptor to engage the inhibitory G proteins (Gi1, Gi2 and Gi3), G13, and β-arrestins. Using bioluminescence resonance energy transfer-based biosensors, we found that, in contrast to the natural CXCR4 ligand, stromal cell-derived factor-1α, which promotes the engagement of the three Gi subtypes, G13 and the two β-arrestins, ATI-2341 leads to the engagement of the Gi subtypes but not G13 or the β-arrestins. Calculation of the transduction ratio for each pathway revealed a strong negative bias of ATI-2341 toward G13 and β-arrestins, revealing functional selectivity for the Gi pathways. The negative bias toward β-arrestins results from the reduced ability of the pepducin to promote GPCR kinase-mediated phosphorylation of the receptor. In addition to revealing ligand-biased signaling of pepducins, these findings shed some light on the mechanism of action of a unique class of allosteric regulators.
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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 | 0.000 |
| 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".