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Record W2051734689 · doi:10.1016/j.ab.2010.11.008

High-performance liquid chromatography separation and intact mass analysis of detergent-solubilized integral membrane proteins

2010· article· en· W2051734689 on OpenAlexfundno aff
G. Berridge, R. Chalk, Nazzareno D’Avanzo, Liang Dong, D. Doyle, Jung-In Kim, Xiaobing Xia, N. Burgess-Brown, Antonio deRiso, Elisabeth P. Carpenter, O. Gileadi

Bibliographic record

VenueAnalytical Biochemistry · 2010
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchKnut och Alice Wallenbergs StiftelseKarolinska InstitutetOntario Ministry of Research, Innovation and ScienceNovartis FoundationStiftelsen för Strategisk ForskningOntario Genomics InstituteCanada Foundation for InnovationWellcome TrustStiftelsen för Strategisk ForskningGenome CanadaOntario Innovation TrustMerckGlaxoSmithKline
KeywordsChromatographyChemistryIntegral membrane proteinMass spectrometryMembrane proteinLiquid chromatography–mass spectrometryMembraneSolubilizationHigh-performance liquid chromatographyQuantitative analysis (chemistry)Biochemistry

Abstract

fetched live from OpenAlex

We have developed a method for intact mass analysis of detergent-solubilized and purified integral membrane proteins using liquid chromatography-mass spectrometry (LC-MS) with methanol as the organic mobile phase. Membrane proteins and detergents are separated chromatographically during the isocratic stage of the gradient profile from a 150-mm C3 reversed-phase column. The mass accuracy is comparable to standard methods employed for soluble proteins; the sensitivity is 10-fold lower, requiring 0.2-5 μg of protein. The method is also compatible with our standard LC-MS method used for intact mass analysis of soluble proteins and may therefore be applied on a multiuser instrument or in a high-throughput environment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.261
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2010
Admission routes1
Has abstractyes

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