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Record W4310639096 · doi:10.1002/1873-3468.14552

Protein engineering of antifreeze proteins reveals that their activity scales with the area of the ice‐binding site

2022· article· en· W4310639096 on OpenAlexafffund
Connor L. Scholl, Peter L. Davies

Bibliographic record

VenueFEBS Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsQueen's University
FundersCanadian Institutes of Health Research
KeywordsAntifreeze proteinAntifreezePolyproline helixChemistryBiophysicsBiochemistryBiologyPeptideOrganic chemistry

Abstract

fetched live from OpenAlex

Antifreeze proteins (AFPs) protect organisms from freezing by binding to ice crystals to prevent their growth. Here, we have investigated how the area of an AFP's ice-binding site (IBS) changes its antifreeze activity. The polyproline type II helical bundle fold of the 9.6-kDa springtail (Collembola) AFP from Granisotoma rainieri (a primitive arthropod) facilitates changes to both IBS length and width. A one quarter decrease in area reduced activity to less than 10%. A one quarter increase in IBS width, through the addition of a single helix, tripled antifreeze activity. However, increasing IBS length by a similar amount actually reduced activity. Expanding the IBS area can greatly increase antifreeze activity but needs to be evaluated by experimentation on a case-by-case basis.

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 score0.271
Threshold uncertainty score0.149

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 head0.015
GPT teacher head0.176
Teacher spread0.161 · 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

Citations17
Published2022
Admission routes2
Has abstractyes

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