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Record W2013560735 · doi:10.1021/ac403723t

Real-Time Monitoring of Membrane-Protein Reconstitution by Isothermal Titration Calorimetry

2013· article· en· W2013560735 on OpenAlexfundno aff
Nadin Jahnke, Oxana Krylova, Torben Hoomann, Carolyn Vargas, Sebastian Fiedler, Peter Pohl, Sandro Keller

Bibliographic record

VenueAnalytical Chemistry · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsnot available
FundersAustrian Science FundUniversity of TorontoDeutsche Forschungsgemeinschaft
KeywordsChemistryIsothermal titration calorimetryTitrationCalorimetryIsothermal processMembraneAmperometric titrationChromatographyThermodynamicsInorganic chemistryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Phase diagrams offer a wealth of thermodynamic information on aqueous mixtures of bilayer-forming lipids and micelle-forming detergents, providing a straightforward means of monitoring and adjusting the supramolecular state of such systems. However, equilibrium phase diagrams are of very limited use for the reconstitution of membrane proteins because of the occurrence of irreversible, unproductive processes such as aggregation and precipitation that compete with productive reconstitution. Here, we exemplify this by dissecting the effects of the K(+) channel KcsA on the process of bilayer self-assembly in a mixture of Escherichia coli polar lipid extract and the nonionic detergent octyl-β-d-glucopyranoside. Even at starting concentrations in the low micromolar range, KcsA has a tremendous impact on the supramolecular organization of the system, shifting the critical lipid/detergent ratios at the onset and completion of vesicle formation by more than 2-fold. Thus, equilibrium phase diagrams obtained for protein-free lipid/detergent mixtures would be misleading when used to guide the reconstitution process. To address this issue, we demonstrate that, even under such nonequilibrium conditions, high-sensitivity isothermal titration calorimetry can be exploited to monitor the progress of membrane-protein reconstitution in real time, in a noninvasive manner, and at high resolution to yield functional proteoliposomes with a narrow size distribution for further downstream 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 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 score0.007
Threshold uncertainty score0.576

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)0.0010.000

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 head0.007
GPT teacher head0.238
Teacher spread0.230 · 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

Citations33
Published2013
Admission routes1
Has abstractyes

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