Helical Antifreeze Proteins Have Independently Evolved in Fishes on Four Occasions
Bibliographic record
Abstract
Alanine-rich α-helical (type I) antifreeze proteins (AFPs) are produced by a variety of fish species from three different orders to protect against freezing in icy seawater. Interspersed amongst and within these orders are fishes making AFPs that are completely different in both sequence and structure. The origin of this variety of types I, II, III and antifreeze glycoproteins (AFGPs) has been attributed to adaptation following sea-level glaciations that occurred after the divergence of most of the extant families of fish. The presence of similar types of AFPs in distantly related fishes has been ascribed to lateral gene transfer in the case of the structurally complex globular type II lectin-like AFPs and to convergent evolution for the AFGPs, which consist of a well-conserved tripeptide repeat. In this paper, we examine the genesis of the type I AFPs, which are intermediate in complexity. These predominantly α-helical peptides share many features, such as putative capping structures, Ala-richness and amphipathic character. We have added to the type I repertoire by cloning additional sequences from sculpin and have found that the similarities between the type I AFPs of the four distinct groups of fishes are not borne out at the nucleotide level. Both the non-coding sequences and the codon usage patterns are strikingly different. We propose that these AFPs arose via convergence from different progenitor helices with a weak affinity for ice and that their similarity is dictated by the propensity of specific amino acids to form helices and to align water on one side of the helix into an ice-like pattern.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".