Theoretical study of interaction of winter flounder antifreeze protein with ice
Bibliographic record
Abstract
Antifreeze proteins (AFPs) are synthesized by various organisms to enable their cells to survive subzero environment. These proteins bind to small ice crystals and inhibit their growth, which if left uncontrolled would be fatal to cells. The crystal structures of a number of AFPs have been determined; however, crystallographic analysis of AFP-ice complex is nearly impossible. Molecular modeling studies of AFPs' interaction with ice surface is therefore invaluable. Early models of AFP-ice interaction suggested H-bond as the primary driving force behind such interaction. Recent experimental evidence, however, suggested that hydrophobic interactions could be the main contributor to AFP-ice association. All computational studies published to date were carried out to verify the H-bond model, and no works attempting to verify the hydrophobic interaction model have been published. In this work, we Monte Carlo-minimized complexes of several AFPs with ice taking into account nonbonded interactions, H-bonds, and the hydration potential for proteins. Parameters of the hydration potential for ice were developed with the assumption that the free energy of the water-ice association should be close to zero at equilibrium melting temperature. Our calculations demonstrate that desolvation of hydrophobic groups in the AFPs upon their binding to the grooves at the ice surface is indeed the major stabilizing contributor to the free energy of AFP-ice binding. This study is consistent with available structural and mutation data on AFPs. In particular, it explains the paradoxical finding that substitution of Thr residues with Val does not affect the potency of winter flounder AFP whereas substitution with Ser abolished its 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".