Different contributions of chemokine N‐terminal features attest to a different ligand binding mode and a bias towards activation of ACKR3/CXCR7 compared with CXCR4 and CXCR3
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Chemokines and their receptors form an intricate interaction and signalling network that plays critical roles in various physiological and pathological cellular processes. The high promiscuity and apparent redundancy of this network makes probing individual chemokine/receptor interactions and functional effects, as well as targeting individual receptor axes for therapeutic applications, challenging. Despite poor sequence identity, the N-terminal regions of chemokines, which play a key role in their activity and selectivity, contain several conserved features. Thus far little is known regarding the molecular basis of their interactions with typical and atypical chemokine receptors or the conservation of their contributions across chemokine-receptor pairs. EXPERIMENTAL APPROACH: We used a broad panel of chemokine variants and modified peptides derived from the N-terminal region of chemokines CXCL12, CXCL11 and vCCL2, to compare the contributions of various features to binding and activation of their shared receptors, the two typical, canonical G protein-signalling receptors, CXCR4 and CXCR3, as well as the atypical scavenger receptor CXCR7/ACKR3, which shows exclusively arrestin-dependent activity. KEY RESULTS: We provide molecular insights into the plasticity of the ligand-binding pockets of these receptors, their chemokine binding modes and their activation mechanisms. Although the chemokine N-terminal region is a critical determinant, neither the most proximal residues nor the N-loop are essential for binding and activation of ACKR3, as distinct from binding and activation of CXCR4 and CXCR3. CONCLUSION AND IMPLICATIONS: These results suggest a different interaction mechanism between this atypical receptor and its ligands and illustrate its strong propensity to activation.
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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) | 0.002 | 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".