Dendrimer-Linked Antifreeze Proteins Have Superior Activity and Thermal Recovery
Bibliographic record
Abstract
By binding to ice, antifreeze proteins (AFPs) depress the freezing point of a solution and inhibit ice recrystallization if freezing does occur. Previous work showed that the activity of an AFP was incrementally increased by fusing it to another protein. Even larger increases in activity were achieved by doubling the number of ice-binding sites by dimerization. Here, we have combined the two strategies by linking multiple outward-facing AFPs to a dendrimer to significantly increase both the size of the molecule and the number of ice-binding sites. Using a heterobifunctional cross-linker, we attached between 6 and 11 type III AFPs to a second-generation polyamidoamine (G2-PAMAM) dendrimer with 16 reactive termini. This heterogeneous sample of dendrimer-linked type III constructs showed a greater than 4-fold increase in freezing point depression over that of monomeric type III AFP. This multimerized AFP was particularly effective at ice recrystallization inhibition activity, likely because it can simultaneously bind multiple ice surfaces. Additionally, attachment to the dendrimer has afforded the AFP superior recovery from heat denaturation. Linking AFPs together via polymers can generate novel reagents for controlling ice growth and recrystallization.
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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) | 0.000 | 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 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".