Specificity for a CCR5 Inhibitor Is Conferred by a Single Amino Acid Residue
Bibliographic record
Abstract
The chemokine receptors CCR5 and CCR2b share 89% amino acid homology. CCR5 is a co-receptor for HIV and CCR5 antagonists have been investigated as inhibitors of HIV infection. We describe the use of two CCR5 antagonists, Schering-C (SCH-C), which is specific for CCR5, and TAK-779, a dual inhibitor of CCR5 and CCR2b, to probe the CCR5 inhibitor binding site using CCR5/CCR2b chimeric receptors. Compound inhibition in the different chimeras was assessed by inhibition of chemokine-induced calcium flux. SCH-C inhibited RANTES (regulated on activation, normal T cell expressed and secreted) (CCL5)-mediated calcium flux on CCR5 with an IC50 of 22.8 nM but was inactive against monocyte chemoattractant protein-1 (CCL2)-mediated calcium flux on CCR2b. However, SCH-C inhibited CCL2-induced calcium flux against a CCR5/CCR2b chimera consisting of transmembrane domains IV-VI of CCR5 with an IC50 of 55 nM. A sequence comparison of CCR5 and CCR2b identified a divergent amino acid sequence located at the junction of transmembrane domain V and second extracellular loop. Transfer of the CCR5 sequence KNFQTLKIV into CCR2b conferred SCH-C inhibition (IC50 of 122 nM) into the predominantly CCR2b chimera. Furthermore, a single substitution, R206I, conferred partial but significant inhibition (IC50 of 1023 nM) by SCH-C. These results show that a limited amino acid sequence is responsible for SCH-C specificity to CCR5, and we propose a model showing the interaction with CCR5 Ile(198).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".