Do diverse antifreeze protein structures bind ice by the same mechanism?
Bibliographic record
Abstract
Antifreeze proteins (AFPs) are characterized by their capacity to adsorb to the surface of ice crystals and prevent their growth. This adsorption lowers the freezing temperature of a solution below its melting point. AFPs have independently evolved in a variety of organisms that may encounter the threat of freezing, including many species of polar fish, insects, plants and microorganisms. Despite their diverse origins and structures we suggest that all AFPs organize ice-like water patterns on one side of the protein (the icebinding site) that then bind the AFP to ice. Here, to help test this hypothesis, we have solved two AFP crystal structures. One is of Lake Ontario midge (Chironomidae) AFP, which has intermediate antifreeze activity. Previously our group modelled the midge AFP based on its sequence characteristics with the crystal structure of Tenebrio molitor AFP as a template. The midge crystal structure at 1.9 -resolution is a close match to the modelled structure and shows a 10-residue repeated solenoid fold, with 8 disulfide-bonds stabilizing the coils and 7 Tyr pointing outward from one side of the solenoid structure as a potential ice-binding site. The second protein crystal structure is from Rhagium mordax, a longhorn beetle, solved at 2.05- resolution. This AFP is hyperactive and its crystal structure resembles that of the Rhagium inquisitor ortholog in having a -solenoid fold with a wide, flat, ice-binding surface formed by four parallel rows of mainly Thr residues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".