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Record W2902142362 · doi:10.1039/c8nr07259k

Tuning coiled coil stability with histidine-metal coordination

2018· article· en· W2902142362 on OpenAlexaff
Isabell Tunn, Alberto Sanz de León, Kerstin G. Blank, Matthew J. Harrington

Bibliographic record

VenueNanoscale · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
FundersInternational Max Planck Research School for Advanced Methods in Process and Systems EngineeringMax-Planck-GesellschaftInternational Max Planck Research School for Environmental, Cellular and Molecular MicrobiologyAlexander von Humboldt-Stiftung
KeywordsHistidineCoiled coilMaterials scienceElectromagnetic coilMetalCoordination complexChemical stabilityNanotechnologyChemistryOrganic chemistryMetallurgyEngineering

Abstract

fetched live from OpenAlex

Coiled coils (CCs) have emerged as versatile building blocks for the synthesis of nanostructures, drug delivery systems and biomimetic hydrogels. Bioengineering metal coordination sites into the terminal ends of a synthetic coiled coil (CC), we generate a nanoscale biological building block with tunable stability. The reversible coordination of Ni2+ thermodynamically stabilizes the CC, as shown with circular dichroism spectroscopy. Using atomic force microscopy-based single-molecule force spectroscopy, it is further shown that Ni2+-binding reinforces the CC mechanically, increasing the barrier height for dissociation. When used as a dynamic crosslink in polyethyleneglycol-based hydrogels, the single-molecule stability of the CC is directly transferred to the bulk material and determines its viscoelastic properties. This reversibly tunable CC, thus, highlights an effective strategy for rationally engineering the single-molecule properties of biomolecular building blocks, which can be translated to the emergent properties of biomimetic materials, as well as other CC containing molecular assemblies.

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 score0.015
Threshold uncertainty score0.399

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 head0.011
GPT teacher head0.255
Teacher spread0.245 · 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

Citations42
Published2018
Admission routes1
Has abstractyes

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