Exploring Platelet Chemokine Antimicrobial Activity: Nuclear Magnetic Resonance Backbone Dynamics of NAP-2 and TC-1
Bibliographic record
Abstract
The platelet chemokines neutrophil-activating peptide-2 (NAP-2) and thrombocidin-1 (TC-1) differ by only two amino acids at their carboxy-terminal ends. Nevertheless, they display a significant difference in their direct antimicrobial activities, with the longer NAP-2 being inactive and TC-1 being active. In an attempt to rationalize this difference in activity, we studied the structure and the dynamics of both proteins by nuclear magnetic resonance (NMR) spectroscopy. Using 15N isotope-labeled protein, we confirmed that the two monomeric proteins essentially have the same overall structure in aqueous solution. However, NMR relaxation measurements provided evidence that the negatively charged carboxy-terminal residues of NAP-2 experience a restricted motion, whereas the carboxy-terminal end of TC-1 moves in an unrestricted manner. The same behavior was also seen in molecular dynamic simulations of both proteins. Detailed analysis of the protein motions through model-free analysis, as well as a determination of their overall correlation times, provided evidence for the existence of a monomer-dimer equilibrium in solution, which seemed to be more prevalent for TC-1. This finding was supported by diffusion NMR experiments. Dimerization generates a larger cationic surface area that would increase the antimicrobial activities of these chemokines. Moreover, these data also show that the negatively charged carboxy-terminal end of NAP-2 (which is absent in TC-1) folds back over part of the positively charged helical region of the protein and, in doing so, interferes with the direct antimicrobial activity.
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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".