19. Determining the Ice-binding Face of an Antifreeze Protein from the Grass, Brachypodium distachyon
Bibliographic record
Abstract
At subzero temperatures extracellular ice growth can kill plants by dehydrating cells and rupturing their membranes. Some grasses can protect themselves from this damage by producing antifreeze proteins (AFPs). These AFPs irreversibly adsorb to growing ice crystals and prevent further gowth. This is measured by ice-recrystallization inhibition (IRI), whereby ice crystals remain small at high sub-zero temperatures. An AFP from Brachypodium distachyon, a temperate grass, has been structurally modelled as a left-handed beta helix with two flat ‘faces’ on either side of the molecule. I am trying to determine which ‘face’ is important for ice adsorption. I have made mutations in the sequence encoding the AFP so that a small, flat amino acid is replaced by a bulky residue, which will likely interfere with the “fit” of the protein to ice. A mutation on one of the flat ‘faces’ of the protein seems to retain all AFP activity, whereas mutations on the opposite ‘face’ appear to cause a loss in activity. Therefore, I believe that this latter ‘face’ is the one important for ice-binding. By understanding how proteins interact with ice, it may be possible to develop new technologies such as environmentally-friendly de-icing agents.
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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 | 0.001 |
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".