Identification of Plant Ice-binding Proteins Through Assessment of Ice-recrystallization Inhibition and Isolation Using Ice-affinity Purification
Bibliographic record
Abstract
Ice-binding proteins (IBPs) belong to a family of stress-induced proteins that are synthesized by certain organisms exposed to subzero temperatures. In plants, freeze damage occurs when extracellular ice crystals grow, resulting in the rupture of plasma membranes and possible cell death. Adsorption of IBPs to ice crystals restricts further growth by a process known as ice-recrystallization inhibition (IRI), thereby reducing cellular damage. IBPs also demonstrate the ability to depress the freezing point of a solution below the equilibrium melting point, a property known as thermal hysteresis (TH) activity. These protective properties have raised interest in the identification of novel IBPs due to their potential use in industrial, medical and agricultural applications. This paper describes the identification of plant IBPs through 1) the induction and extraction of IBPs in plant tissue, 2) the screening of extracts for IRI activity, and 3) the isolation and purification of IBPs. Following the induction of IBPs by low temperature exposure, extracts are tested for IRI activity using a 'splat assay', which allows the observation of ice crystal growth using a standard light microscope. This assay requires a low protein concentration and generates results that are quickly obtained and easily interpreted, providing an initial screen for ice binding activity. IBPs can then be isolated from contaminating proteins by utilizing the property of IBPs to adsorb to ice, through a technique called 'ice-affinity purification'. Using cell lysates collected from plant extracts, an ice hemisphere can be slowly grown on a brass probe. This incorporates IBPs into the crystalline structure of the polycrystalline ice. Requiring no a priori biochemical or structural knowledge of the IBP, this method allows for recovery of active protein. Ice-purified protein fractions can be used for downstream applications including the identification of peptide sequences by mass spectrometry and the biochemical analysis of native proteins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".