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Record W1993511239 · doi:10.1002/rcm.1423

Effects of common surfactants on protein digestion and matrix‐assisted laser desorption/ionization mass spectrometric analysis of the digested peptides using two‐layer sample preparation

2004· article· en· W1993511239 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2004
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryChromatographyDesorptionSample preparationMatrix-assisted laser desorption/ionizationMatrix (chemical analysis)IonizationSample preparation in mass spectrometryMass spectrometryDigestion (alchemy)Analytical Chemistry (journal)Organic chemistryElectrospray ionizationAdsorptionIon

Abstract

fetched live from OpenAlex

While surfactants are commonly used in preparing protein samples, their presence in a protein sample can potentially affect the enzymatic digestion process and the subsequent analysis of the resulting peptides by mass spectrometry. The extent of the tolerance of matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) to surfactant interference in peptide analysis is very much dependent on the matrix/sample preparation method. In this work the effects of four commonly used surfactants, namely n-octyl glucoside (OG), Triton X-100 (TX-100), 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS) and sodium dodecyl sulfate (SDS), for biological sample preparation on trypsin digestion and MALDI-MS of the resulting digest are examined in detail within the context of using a two-layer method for MALDI matrix/sample preparation. Non-ionic and mild surfactants, such as OG, TX-100 or CHAPS, are found to have no significant effect on trypsin digestion with surfactant concentrations up to 1%. However, TX-100 and CHAPS interfere with the subsequent peptide analysis by MALDI-MS and should be removed prior to peptide analysis. OG is an MS-friendly surfactant and no effect is observed for MALDI peptide analysis. The effect of SDS on trypsin digestion in terms of the number of peptides generated and the overall protein sequence coverage by these peptides is found to be protein dependent. The use of SDS to solubilize hydrophobic membrane proteins, followed by trypsin digestion in the presence of 0.1% SDS, results in a peptide mixture that can be analyzed directly by MALDI-MS. These peptides are shown to provide better sequence coverage compared with those obtained without the use of SDS in the case of bacteriorhodopsin, a very hydrophobic transmembrane protein. This work illustrates that MALDI-MS with the two-layer sample preparation method can be used for direct analysis of protein digests with no or minimum sample cleanup after proteins are digested in a solution containing surfactants.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.010
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.312
Teacher spread0.289 · 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