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Record W1991442928 · doi:10.1371/journal.pone.0015682

NMR Characterizations of the Ice Binding Surface of an Antifreeze Protein

2010· article· en· W1991442928 on OpenAlexaff
Yunfei Hu, Congmin Li, Zongchao Jia, Changwen Jin

Bibliographic record

VenuePLoS ONE · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsQueen's University
FundersPeking UniversityNational Natural Science Foundation of China
KeywordsAntifreeze proteinChemistryDimerAntifreezePopulationCrystallographyOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Antifreeze protein (AFP) has a unique function of reducing solution freezing temperature to protect organisms from ice damage. However, its functional mechanism is not well understood. An intriguing question concerning AFP function is how the high selectivity for ice ligand is achieved in the presence of free water of much higher concentration which likely imposes a large kinetic barrier for protein-ice recognition. In this study, we explore this question by investigating the property of the ice binding surface of an antifreeze protein using NMR spectroscopy. An investigation of the temperature gradient of amide proton chemical shift and its correlation with chemical shift deviation from random coil was performed for CfAFP-501, a hyperactive insect AFP. A good correlation between the two parameters was observed for one of the two Thr rows on the ice binding surface. A significant temperature-dependent protein-solvent interaction is found to be the most probable origin for this correlation, which is consistent with a scenario of hydrophobic hydration on the ice binding surface. In accordance with this finding, rotational correlation time analyses combined with relaxation dispersion measurements reveals a weak dimer formation through ice binding surface at room temperature and a population shift of dimer to monomer at low temperature, suggesting hydrophobic effect involved in dimer formation and hence hydrophobic hydration on the ice binding surface of the protein. Our finding of hydrophobic hydration on the ice binding surface provides a test for existing simulation studies. The occurrence of hydrophobic hydration on the ice binding surface is likely unnecessary for enhancing protein-ice binding affinity which is achieved by a tight H-bonding network. Subsequently, we speculate that the hydrophobic hydration occurring on the ice binding surface plays a role in facilitating protein-ice recognition by lowering the kinetic barrier as suggested by some simulation studies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.203
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2010
Admission routes1
Has abstractyes

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