Paradoxical effects on ice nucleation are intrinsic to a small winter flounder antifreeze protein
Bibliographic record
Abstract
Antifreeze proteins (AFPs) bind to ice in solutions, resulting in non-colligative freezing point depression; however, their effects on ice nucleation are not well understood. The predominant plasma AFP of winter flounder (Pseudopleuronectes americanus) is AFP6, which is an amphiphilic alpha helix. In this study, AFP6 and modified constructs were produced as fusion proteins in Escherichia coli, subjected to proteolysis when required and purified prior to use. AFP6 and its recombinant fusion precursor generated similar thermal hysteresis and bipyramidal ice crystals, whereas an inactive mutant AFP6 produced hexagonal crystals and no hysteresis. Circular dichroism spectra of the wild-type and mutant AFP6 were consistent with an alpha helix. The effects of these proteins on ice nucleation were investigated alongside non-AFP proteins using a standard droplet freezing assay. In the presence of nucleating AgI, modest reductions in the nucleation temperature occurred with the addition of mutant AFP6, and several non-AFPs, suggesting non-specific inhibition of AgI-induced ice nucleation. In these experiments, both AFP6 and its recombinant precursor resulted in lower nucleation temperatures, consistent with an additional inhibitory effect. Conversely, in the absence of AgI, AFP6 induced ice nucleation, with no other proteins showing this effect. Nucleation by AFP6 was dose-dependent, reaching a maximum at 1.5 mM protein. Nucleation by AFP6 also required an ice-binding site, as the inactive mutant had no effect. Furthermore, the absence of nucleation by the recombinant precursor protein suggested that the fusion moiety was interfering with the formation of a surface capable of nucleating ice.
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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".