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
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it