Novel dimeric β-helical model of an ice nucleation protein with bridged active sites
Bibliographic record
Abstract
BACKGROUND: Ice nucleation proteins (INPs) allow water to freeze at high subzero temperatures. Due to their large size (>120 kDa), membrane association, and tendency to aggregate, an experimentally-determined tertiary structure of an INP has yet to be reported. How they function at the molecular level therefore remains unknown. RESULTS: Here we have predicted a novel β-helical fold for the INP produced by the bacterium Pseudomonas borealis. The protein uses internal serine and glutamine ladders for stabilization and is predicted to dimerize via the burying of a solvent-exposed tyrosine ladder to make an intimate hydrophobic contact along the dimerization interface. The manner in which PbINP dimerizes also allows for its multimerization, which could explain the aggregation-dependence of INP activity. Both sides of the PbINP structure have tandem arrays of amino acids that can organize waters into the ice-like clathrate structures seen on antifreeze proteins. CONCLUSIONS: Dimerization dramatically increases the 'ice-active' surface area of the protein by doubling its width, increasing its length, and presenting identical ice-forming surfaces on both sides of the protein. We suggest that this allows sufficient anchored clathrate waters to align on the INP surface to nucleate freezing. As PbINP is highly similar to all known bacterial INPs, we predict its fold and mechanism of action will apply to these other INPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".