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Record W2327486514 · doi:10.1021/pr500864a

Resolubilization of Precipitated Intact Membrane Proteins with Cold Formic Acid for Analysis by Mass Spectrometry

2014· article· en· W2327486514 on OpenAlexafffund
Alan A. Doucette, Douglas B. Vieira, Dennis J. Orton, Mark J. Wall

Bibliographic record

VenueJournal of Proteome Research · 2014
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFormic acidChemistryChromatographyProtein precipitationSolventSodium dodecyl sulfateAcetonePrecipitationMass spectrometryPelletMembrane proteinMembraneBiochemistryBiology

Abstract

fetched live from OpenAlex

Protein precipitation in organic solvent is an effective strategy to deplete sodium dodecyl sulfate (SDS) ahead of MS analysis. Here we evaluate the recovery of membrane and water-soluble proteins through precipitation with chloroform/methanol/water or with acetone (80%). With each solvent system, membrane protein recovery was greater than 90%, which was generally higher than that of cytosolic proteins. With few exceptions, residual supernatant proteins detected by MS were also detected in the precipitation pellet, having higher MS signal intensity in the pellet fraction. Following precipitation, we present a novel strategy for the quantitative resolubilization of proteins in an MS-compatible solvent system. The pellet is incubated at -20 °C in 80% formic acid/water and then diluted 10-fold with water. Membrane protein recovery matches that of sonication of the pellet in 1% SDS. The resolubilized proteins are stable at room temperature, with no observed formylation as is typical of proteins suspended in formic acid at room temperature. The protocol is applied to the molecular weight determination of membrane proteins from a GELFrEE-fractionated sample of Escherichia coli proteins.

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)0.003
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.474
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.340
Teacher spread0.309 · 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

Citations63
Published2014
Admission routes2
Has abstractyes

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