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Record W2047978531 · doi:10.1021/ac010486h

Recovery of Gel-Separated Proteins for In-Solution Digestion and Mass Spectrometry

2001· article· en· W2047978531 on OpenAlexfundno aff
P. Andreas Jonsson, Youssef Aissouni, Carina Palmberg, Piergiorgio Percipalle, Erik Nordling, Bertil Daneholt, Hans Jörnvall, Tomas Bergman

Bibliographic record

VenueAnalytical Chemistry · 2001
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsnot available
FundersCancerfondenCancer Research Society
KeywordsChemistryChromatographyElectroblottingSample preparation in mass spectrometryMass spectrometryProtein mass spectrometryBottom-up proteomicsTandem mass spectrometryMatrix-assisted laser desorption/ionizationTrifluoroacetic acidElectrospray ionizationCapillary electrophoresis–mass spectrometrySample preparationBiochemistryPolyacrylamide gel electrophoresisDesorptionOrganic chemistry

Abstract

fetched live from OpenAlex

A protocol for mass spectrometry of gel-separated proteins resulting in significantly increased sequence coverage and in improved possibilities for detection and identification of posttranslational modifications was developed. In relation to the standard in-gel digestion procedure, the sequence coverage using a combination of matrix-assisted laser desorption/ionization and electrospray ionization mass spectrometry was on the average increased by 30%. The method involves electroblotting of the gel-separated proteins to a poly(vinylidene difluoride) membrane. The proteins are extracted from the membrane using a solution of 1% trifluoroacetic acid in 70% acetonitrile and lyophilized. After reconstitution of the protein extract in digestion buffer, proteolytic cleavage is carried out in-solution as opposed to the standard in-gel digestion procedure. This allows recovery of large and hydrophobic peptides for mass spectrometry and reduces the risk for entrapment of proteolytic peptides in the gel matrix. The method was applied to proteins in the 30-40-kDa range with highly different structural properties. The improved ability to localize and determine protein modifications is shown for N-terminal acetylation and methylation of a histidine residue. Furthermore, the method enables fast screening of homologous protein sequences.

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 score0.073
Threshold uncertainty score0.480

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 head0.014
GPT teacher head0.284
Teacher spread0.270 · 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

Citations40
Published2001
Admission routes1
Has abstractyes

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