Properties and biotechnological applications of ice-binding proteins in bacteria
Bibliographic record
Abstract
Ice-binding proteins (IBPs), such as antifreeze proteins (AFPs) and ice-nucleating proteins (INPs), have been described in diverse cold-adapted organisms, and their potential applications in biotechnology have been recognized in various fields. Currently, both IBPs are being applied to biotechnological processes, primarily in medicine and the food industry. However, our knowledge regarding the diversity of bacterial IBPs is limited; few studies have purified and characterized AFPs and INPs from bacteria. Phenotypically verified IBPs have been described in members belonging to Gammaproteobacteria, Actinobacteria and Flavobacteriia classes, whereas putative IBPs have been found in Gammaproteobacteria, Alphaproteobacteria and Bacilli classes. Thus, the main goal of this minireview is to summarize the current information on bacterial IBPs and their application in biotechnology, emphasizing the potential application in less explored fields such as agriculture. Investigations have suggested the use of INP-producing bacteria antagonists and AFPs-producing bacteria (or their AFPs) as a very attractive strategy to prevent frost damages in crops. UniProt database analyses of reported IBPs (phenotypically verified) and putative IBPs also show the limited information available on bacterial IBPs and indicate that major studies are required.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".