A Dual Detergent Strategy to Capture a Bacterial Outer Membrane Proteome in Peptidiscs for Characterization by Mass Spectrometry and Binding Assays
Bibliographic record
Abstract
The outer membrane of Gram-negative bacteria plays a critical role in protecting the cell against external stressors, including antibiotics, and therefore is a prime target for antimicrobial discovery. To facilitate the discovery efforts, a precise knowledge of the outer membrane proteome, and possible variations during pathogenesis, is important. Characterization of the bacterial outer membrane remain challenging, however, and low throughput, due to the high hydrophobicity and relatively low abundance of this cell compartment. Here we adapt our peptidisc-based method to selectively isolate the outer membrane proteome before analysis by mass spectrometry. Using a dual detergent membrane solubilization approach, followed by protein purification in peptidiscs, we capture over 70 outer membrane proteins, including 26 integral β-barrels and 26 lipoproteins. Many of these proteins are present at high peptide intensities, indicative of a high abundance in the library sample. We further show that the isolated outer membrane proteome can be employed in downstream ligand-binding assays. This peptidisc library made of outer membrane proteins may therefore be useful to systematically survey other bacterial outer membrane proteomes, but also as a nanoparticle format able to support the discovery of next-generation antimicrobials. Data are available via ProteomeXchange identifier PXD036749.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".