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Record W2135448686 · doi:10.1039/c002970j

High water mobility on the ice-binding surface of a hyperactive antifreeze protein

2010· article· en· W2135448686 on OpenAlexafffund
Kristofer Modig, Johan �Qvist, Christopher B. Marshall, Peter L. Davies, Bertil Halle

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsQueen's University
FundersCanadian Institutes of Health Research
KeywordsAntifreeze proteinChemistryIce crystalsMoleculeAntifreezeChemical physicsMolecular dynamicsNanosecondDispersion (optics)CrystallographyComputational chemistryOrganic chemistryBiochemistryMeteorology

Abstract

fetched live from OpenAlex

Antifreeze proteins (AFPs) prevent uncontrolled ice formation in organisms exposed to subzero temperatures by binding irreversibly to specific planes of nascent ice crystals. To understand the thermodynamic driving forces and kinetic mechanism of AFP activity, it is necessary to characterize the hydration behavior of these proteins in solution. With this aim, we have studied the hyperactive insect AFP from Tenebrio molitor (TmAFP) with the (17)O magnetic relaxation dispersion (MRD) method, which selectively monitors the rotational motion and exchange kinetics of water molecules on picosecond-microsecond time scales. The global hydration behavior of TmAFP is found to be similar to non-antifreeze proteins, with no evidence of ice-like or long-ranged modifications of the solvent. However, two sets of structural water molecules, located within the core and on the ice-binding face in the crystal structure of TmAFP, may have functional significance. We find that 2 of the 5 internal water molecules exchange with a residence time of 8 +/- 1 micros at 300 K and a large activation energy of approximately 50 kJ mol(-1), reflecting intermittent large-scale conformational fluctuations in this exceptionally dense and rigid protein. Six water molecules arrayed with ice-like spacing in the central trough on the ice-binding face exchange with bulk water on a sub-nanosecond time scale. The combination of high order and fast exchange may allow these water molecules to contribute entropically to the ice-binding affinity without limiting the absorption rate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.218
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations60
Published2010
Admission routes2
Has abstractyes

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