Automated SDS Depletion for Mass Spectrometry of Intact Membrane Proteins though Transmembrane Electrophoresis
Bibliographic record
Abstract
Membrane proteins are underrepresented in proteome analysis platforms because of their hydrophobic character, contributing to decreased solubility. Sodium dodecyl sulfate is a favored denaturant in proteomic workflows, facilitating cell lysis and protein dissolution; however, SDS impedes MS detection and therefore must be removed prior to analysis. Although strategies exist for SDS removal, they provide low recovery, purity, or reproducibility. Here we present a simple automated device, termed transmembrane electrophoresis (TME), incorporating the principles of membrane filtration, but with an applied electric current to ensure near-complete (99.9%) removal of the surfactant, including protein-bound SDS. Intact proteins are recovered in solution phase in high yield (90-100%) within 1 h of operation. The strategy is applied to protein standards and proteome mixtures, including an enriched membrane fraction from E. coli, resulting in quality MS spectra free of SDS adducts. The TME platform is applicable to both bottom-up MS/MS as well as LC-ESI-MS analysis of intact proteins. SDS-depleted fractions reveal a similar number of protein identifications (285) compared wit a non-SDS control (280), being highly correlated in terms of protein spectral counts. This fully automated approach to SDS removal presents a viable tool for proteome sample processing ahead of MS analysis. Data are available via ProteomeXchange, identifier PXD003941.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".