High water mobility on the ice-binding surface of a hyperactive antifreeze protein
Bibliographic record
Abstract
Antifreeze proteins (AFPs) prevent uncontrolled ice formation in organisms exposed to subzero temperatures by binding irreversibly to specific planes of nascent ice crystals. To understand the thermodynamic driving forces and kinetic mechanism of AFP activity, it is necessary to characterize the hydration behavior of these proteins in solution. With this aim, we have studied the hyperactive insect AFP from Tenebrio molitor (TmAFP) with the (17)O magnetic relaxation dispersion (MRD) method, which selectively monitors the rotational motion and exchange kinetics of water molecules on picosecond-microsecond time scales. The global hydration behavior of TmAFP is found to be similar to non-antifreeze proteins, with no evidence of ice-like or long-ranged modifications of the solvent. However, two sets of structural water molecules, located within the core and on the ice-binding face in the crystal structure of TmAFP, may have functional significance. We find that 2 of the 5 internal water molecules exchange with a residence time of 8 +/- 1 micros at 300 K and a large activation energy of approximately 50 kJ mol(-1), reflecting intermittent large-scale conformational fluctuations in this exceptionally dense and rigid protein. Six water molecules arrayed with ice-like spacing in the central trough on the ice-binding face exchange with bulk water on a sub-nanosecond time scale. The combination of high order and fast exchange may allow these water molecules to contribute entropically to the ice-binding affinity without limiting the absorption rate.
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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) | 0.000 | 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".