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Record W4388733972 · doi:10.7554/elife.91976.2

Ice nucleation proteins self-assemble into large fibres to trigger freezing at near 0 °C

2023· preprint· en· W4388733972 on OpenAlexaff
Thomas Hansen, Jocelyn C. Lee, Nàama Reicher, Gil Ovadia, Shuaiqi Guo, Wangbiao Guo, Jun Liu, Ido Braslavsky, Yinon Rudich, Peter L. Davies

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsQueen's University
Fundersnot available
KeywordsIce nucleusNucleationAmorphous iceFrost (temperature)Chemical physicsAntiparallel (mathematics)ChemistryCrystallographyIce crystalsBiophysicsMaterials scienceChemical engineeringAmorphous solidPhysicsBiology

Abstract

fetched live from OpenAlex

Abstract In nature, frost can form at a few degrees below 0 °C. However, this process requires the assembly of tens of thousands of ice-like water molecules that align together to initiate freezing at these relatively high temperatures. Water ordering on this scale is mediated by the ice nucleation proteins of common environmental bacteria like Pseudomonas syringae and P. borealis. However, individually, these 100-kDa proteins are too small to organize enough water molecules for frost formation, and it is not known how giant, megadalton-sized multimers, which are crucial for ice nucleation at high sub-zero temperatures, form. The ability of multimers to self-assemble was suggested when the transfer of an ice nucleation protein gene into Escherichia coli led to efficient ice nucleation. Here we demonstrate that a positively-charged sub-domain at the C-terminal end of the central beta-solenoid of the ice nucleation protein is crucial for multimerization. Truncation, relocation, or change of the charge of this subdomain caused a catastrophic loss of ice nucleation ability. Cryo-electron tomography of the recombinant E. coli showed that the ice nucleation protein multimers form fibres that are ∼ 5 nm across and up to 200 nm long. A model of these fibres as an overlapping series of antiparallel dimers can account for all their known properties and suggests a route to making cell-free ice nucleators for biotechnological applications.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.030

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 head0.027
GPT teacher head0.269
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same topicnanoparticles nucleation surface interactionsFrench-language works237,207