Selectivity of synthetic CXCR4 agonists
Bibliographic record
Abstract
CXCR4, a widely distributed chemokine receptor, is known for its major role in cancer metastasis and AIDS pathogenesis. Pharmacotherapy with antagonistic ligands is greatly hindered since it blocks the many essential physiological roles of CXCR4, causing serious undesired effects. CXCR4 has only one known endogenous ligand, CXCL12, a 68 amino acid peptide that only binds to CXCR4 and CXCR7. To improve this situation we have discovered CXCR4 agonists from in silico homology modeling and the crystal structure of CXCR4: we hypothesized that peptide chimera of T140 (a potent inverse agonist of CXCR4) and the N‐terminal fragments of CXCL12 could act as agonists on CXCR4; such compounds proved to be the first potent synthetic CXCR4 agonists. We have shown that these synthetic peptides were able to induce chemotaxis both in vitro and in vivo, and to activate the typical Gi signaling pathway of CXCR4 in nanomolar concentrations, similarly to CXCL12. This study aims to assess the CXCR4 selectivity of these synthetic agonists. Binding assays on CXCR7 and CXCR4 were performed and showed affinities nanomolar for CXCR4 but micromolar for CXCR7, indicating up to a thousand‐fold selectivity. Functional selectivity was assessed on β‐arrestin‐2 recruitment to CXCR4 and to CXCR7. The surprising result is that our synthetic CXCR4 agonists appear to induce β‐arrestin recruitment on CXCR7 only and not on CXCR4.
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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) | 0.003 | 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".