Top-Down and Bottom-Up Proteomics of SDS-Containing Solutions Following Mass-Based Separation
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.
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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