Revealing Surface Waters on an Antifreeze Protein by Fusion Protein Crystallography Combined with Molecular Dynamic Simulations
Bibliographic record
Abstract
Antifreeze proteins (AFPs) adsorb to ice through an extensive, flat, relatively hydrophobic surface. It has been suggested that this ice-binding site (IBS) organizes surface waters into an ice-like clathrate arrangement that matches and fuses to the quasi-liquid layer on the ice surface. On cooling, these waters join the ice lattice and freeze the AFP to its ligand. Evidence for the generality of this binding mechanism is limited because AFPs tend to crystallize with their IBS as a preferred protein-protein contact surface, which displaces some bound waters. Type III AFP is a 7 kDa globular protein with an IBS made up two adjacent surfaces. In the crystal structure of the most active isoform (QAE1), the part of the IBS that docks to the primary prism plane of ice is partially exposed to solvent and has clathrate waters present that match this plane of ice. The adjacent IBS, which matches the pyramidal plane of ice, is involved in protein-protein crystal contacts with few surface waters. Here we have changed the protein-protein contacts in the ice-binding region by crystallizing a fusion of QAE1 to maltose-binding protein. In this 1.9 Å structure, the IBS that fits the pyramidal plane of ice is exposed to solvent. By combining crystallography data with MD simulations, the surface waters on both sides of the IBS were revealed and match well with the target ice planes. The waters on the pyramidal plane IBS were loosely constrained, which might explain why other isoforms of type III AFP that lack the prism plane IBS are less active than QAE1. The AFP fusion crystallization method can potentially be used to force the exposure to solvent of the IBS on other AFPs to reveal the locations of key surface waters.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".