The Role of N and C Termini in the Antifreeze Activity of Winter Flounder (Pleuronectes americanus) Antifreeze Proteins
Bibliographic record
Abstract
Antifreeze proteins (AFPs) are found in many marine fish and have been classified into five biochemical classes: AFP types I-IV and the antifreeze glycoproteins. Type I AFPs are alpha-helical, partially amphipathic, Ala-rich polypeptides. The winter flounder (Pleuronectes americanus) produces two type I AFP subclasses, the liver-type AFPs (wflAFPs) and the skin-type AFPs (wfsAFPs), that are encoded by distinct gene families with different tissue-specific expression. wfsAFPs and wflAFPs share a high level of identity even though the wfsAFPs have approximately half the activity of the wflAFPs. Synthetic polypeptides based on two representative wflAFPs and wfsAFPs were generated to examine the role of the termini in antifreeze activity. Through systematic exchange of N and C termini between wflAFP-6 and wfsAFP-2, the termini were determined to be the major causative agents for the variation in activity levels between the two AFPs. Furthermore, the termini of wflAFP-6 possessed greater helix-stabilizing ability compared with their wfsAFP-2 counterparts. The observed 50% difference in activity between wflAFP-6 and wfsAFP-2 can be divided into approximately 20% for differences at each termini and approximately 10% for differences in the core. Furthermore, the N terminus was determined to be the most critical component for antifreeze activity.
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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".