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Record W2090392341 · doi:10.1074/jbc.m113.450973

Crystal Structure of an Insect Antifreeze Protein and Its Implications for Ice Binding

2013· article· en· W2090392341 on OpenAlexafffund
Aaron Hakim, Jennifer B. Nguyen, Koli Basu, Darren F. Zhu, Durga Thakral, Peter L. Davies, Farren J. Isaacs, Yorgo Modis, Wuyi Meng

Bibliographic record

VenueJournal of Biological Chemistry · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsQueen's University
FundersNational Institute of General Medical SciencesCanadian Institutes of Health Research
KeywordsAntifreeze proteinIce crystalsCrystallographyChemistryAmorphous iceAntifreezeMolecular dynamicsCrystal structureChemical physicsMaterials scienceComputational chemistryBiochemistryPhysicsOrganic chemistryAmorphous solid

Abstract

fetched live from OpenAlex

Antifreeze proteins (AFPs) help some organisms resist freezing by binding to ice crystals and inhibiting their growth. The molecular basis for how these proteins recognize and bind ice is not well understood. The longhorn beetle Rhagium inquisitor can supercool to below −25 °C, in part by synthesizing the most potent antifreeze protein studied thus far (RiAFP). We report the crystal structure of the 13-kDa RiAFP, determined at 1.21 Å resolution using direct methods. The structure, which contains 1,914 nonhydrogen protein atoms in the asymmetric unit, is the largest determined ab initio without heavy atoms. It reveals a compressed β-solenoid fold in which the top and bottom sheets are held together by a silk-like interdigitation of short side chains. RiAFP is perhaps the most regular structure yet observed. It is a second independently evolved AFP type in beetles. The two beetle AFPs have in common an extremely flat ice-binding surface comprising regular outward-projecting parallel arrays of threonine residues. The more active, wider RiAFP has four (rather than two) of these arrays between which the crystal structure shows the presence of ice-like waters. Molecular dynamics simulations independently reproduce the locations of these ordered crystallographic waters and predict additional waters that together provide an extensive view of the AFP interaction with ice. By matching several planes of hexagonal ice, these waters may help freeze the AFP to the ice surface, thus providing the molecular basis of ice binding. Antifreeze proteins (AFPs) help some organisms resist freezing by binding to ice crystals and inhibiting their growth. The molecular basis for how these proteins recognize and bind ice is not well understood. The longhorn beetle Rhagium inquisitor can supercool to below −25 °C, in part by synthesizing the most potent antifreeze protein studied thus far (RiAFP). We report the crystal structure of the 13-kDa RiAFP, determined at 1.21 Å resolution using direct methods. The structure, which contains 1,914 nonhydrogen protein atoms in the asymmetric unit, is the largest determined ab initio without heavy atoms. It reveals a compressed β-solenoid fold in which the top and bottom sheets are held together by a silk-like interdigitation of short side chains. RiAFP is perhaps the most regular structure yet observed. It is a second independently evolved AFP type in beetles. The two beetle AFPs have in common an extremely flat ice-binding surface comprising regular outward-projecting parallel arrays of threonine residues. The more active, wider RiAFP has four (rather than two) of these arrays between which the crystal structure shows the presence of ice-like waters. Molecular dynamics simulations independently reproduce the locations of these ordered crystallographic waters and predict additional waters that together provide an extensive view of the AFP interaction with ice. By matching several planes of hexagonal ice, these waters may help freeze the AFP to the ice surface, thus providing the molecular basis of ice binding.

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 score0.085
Threshold uncertainty score0.546

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.244
Teacher spread0.214 · 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

Citations130
Published2013
Admission routes2
Has abstractyes

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