Top-Down and Bottom-Up Proteomics of SDS-Containing Solutions Following Mass-Based Separation
Bibliographic record
Abstract
SDS has recognized benefits for protein sample preparation, including solubilization and imparting molecular weight separation (e.g., SDS-PAGE). Here, we compare two proteome workflows which incorporate SDS for protein separation, namely, SDS-PAGE coupled to LC/MS (GeLC MS), along with a solution separation platform, GELFrEE, for intact proteome prefractionation and identification. Despite the clear importance of SDS in these and other proteome analysis workflows, the affect of SDS on an LC/MS proteome experiment has not been quantified. We first examined the influence of SDS on both a bottom-up as well as a top-down (intact protein) MS workflow. Surprisingly, at levels up to 0.01% SDS in the injected sample, reliable MS characterization is obtained. We subsequently explored organic precipitation protocols (chloroform/methanol/water and acetone) as a means of lowering SDS, and present a simple modified acetone precipitation protocol which consistently enables MS proteome characterizations from samples initially containing 2% SDS. With this effective strategy for SDS reduction, the GELFrEE MS workflow for bottom-up proteome analysis was characterized relative to GeLC MS. Remarkable agreement in the number and type of identified proteins was obtained from these two separation platforms, validating the use of SDS in solution-phase proteome analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".