Comparison of SDS‐ and methanol‐assisted protein solubilization and digestion methods for <b><i>Escherichia coli</i></b> membrane proteome analysis by 2‐D LC‐MS/MS
Bibliographic record
Abstract
Both organic solvent and surfactant have been used for dissolving membrane proteins for shotgun proteomics. In this work, two methods of protein solubilization, namely using 60% methanol or 1% SDS, to dissolve and analyze the inner membrane fraction of an Escherichia coli K12 cell lysate were compared. A total of 358 proteins (1417 unique peptides) from the methanol-solubilized protein mixture and 299 proteins (892 peptides) from the SDS-solubilized sample-were identified by using trypsin digestion and 2-D LC-ESI MS/MS. It was found that the methanol method detected more hydrophobic peptides, resulting in a greater number of proteins identified, than the SDS method. We found that 159 out of 358 proteins (44%) and 120 out of 299 proteins (40%) detected from the methanol- and SDS-solubilized samples, respectively, are integral membrane proteins. Among the 190 integral membrane proteins 70 were identified exclusively in the methanol-solubilized sample, 89 were identified by both methods, and only 31 proteins were exclusively identified by the SDS method. It is shown that the integral membrane proteins reflected the theoretical proteome for number of transmembrane helices, length, functional class, and topology, indicating there was no bias in the proteins identified.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".