When Are Antifreeze Proteins in Solution Essential for Ice Growth Inhibition?
Bibliographic record
Abstract
Antifreeze proteins (AFPs) are a widespread class of proteins that bind to ice and facilitate the survival of organisms under freezing conditions. AFPs have enormous potential in applications that require control over ice growth. However, the nature of the binding interaction between AFPs and ice remains the subject of debate. Using a microfluidics system developed in-house we previously showed that hyperactive AFP from the Tenebrio molitor beetle, TmAFP, remains bound to an ice crystal surface after exchanging the solution surrounding the ice crystal to an AFP-free solution. Furthermore, these surface-adsorbed TmAFP molecules sufficed to prevent ice growth. These experiments provided compelling evidence for the irreversible binding of hyperactive AFPs to ice. Here, we tested a moderately active type III AFP (AFPIII) from a fish in a similar microfluidics system. We found, in solution exchange experiments that the AFPIIIs were also irreversibly bound to the ice crystals. However, some crystals displayed "burst" growth during the solution exchange. AFPIII, like other moderately active fish AFPs, is unable to bind to the basal plane of an ice crystal. We showed that although moderate AFPs bound to ice irreversibly, moderate AFPs in solution were needed to inhibit ice growth from the bipyramidal crystal tips. Instead of binding to the basal plane, these AFPs minimized the basal face size by stabilizing other crystal planes that converge to form the crystal tips. Furthermore, when access of solution to the basal plane was physically blocked by the microfluidics device walls, we observed enhancement of the antifreeze activity. These findings provide direct evidence that the weak point of ice growth inhibition by fish AFPs is the basal plane, whereas insect AFPs, which can bind to the basal plane, are able to inhibit its growth and thereby increase antifreeze activity.
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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".