Partitioning of Fish and Insect Antifreeze Proteins into Ice Suggests They Bind with Comparable Affinity
Bibliographic record
Abstract
Antifreeze proteins (AFPs) inhibit the growth of ice by binding to the surface of ice crystals, preventing the addition of water molecules to cause a local depression of the freezing point. AFPs from insects are much more effective at depressing the freezing point than fish AFPs. Here, we have investigated the possibility that insect AFPs bind more avidly to ice than fish AFPs. Because it is not possible to directly measure the affinity of an AFP for ice, we have assessed binding indirectly by examining the partitioning of proteins into a slowly growing ice hemisphere. AFP molecules adsorbed to the surface and became incorporated into the ice as they were overgrown. Solutes, including non-AFPs, were very efficiently excluded from ice, whereas AFPs became incorporated into ice at a concentration roughly equal to that of the original solution, and this was independent of the AFP concentration in the range (submillimolar) tested. Despite their >10-fold difference in antifreeze activity, fish and insect AFPs partitioned into ice to a similar degree, suggesting that insect AFPs do not bind to ice with appreciably higher affinity. Additionally, we have demonstrated that steric mutations on the ice binding surface that decrease the antifreeze activity of an AFP also reduce its inclusion into ice, supporting the validity of using partitioning measurements to assess a protein's affinity for ice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".