Two-Layer Sample Preparation Method for MALDI Mass Spectrometric Analysis of Protein and Peptide Samples Containing Sodium Dodecyl Sulfate
Bibliographic record
Abstract
Sodium dodecyl sulfate (SDS) is widely used in protein sample workup. However, many mass spectrometric methods cannot tolerate the presence of this strong surfactant in a protein sample. We present a practical and robust technique based on a two-layer matrix/sample deposition method for the analysis of protein and peptide samples containing SDS by matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). The two-layer method involves the deposition of a mixture of sample and matrix on top of a thin layer of matrix crystals. It was found that for SDS-containing samples, the intensity of the MALDI signals can be affected by the conditions of sample preparation: on-probe washing, choice of matrix, deposition method, solvent system, and protein-to-SDS ratio. However, we found that, under appropriate conditions, the two-layer method gave reliable MALDI signals for samples with levels of SDS up to approximately 1%. The applications of this method are demonstrated for MALDI analysis of hydrophobic membrane proteins as well as bacterial extracts. We envision that this two-layer method capable of handling impure samples including those containing SDS will play an important role in protein molecular weight analysis as well as in proteome identification by MALDI-MS and MS/MS.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".