Mass Spectrometry of Bacterial Outer Membrane Enzymes within the Native Lipid Environment
Bibliographic record
Abstract
Lipid complexity of bacterial membranes and the dynamic interaction of lipophilic substrates with membrane‐embedded enzymes reflect a demand for a robust detection approach that unravels the local lipids and hydrophobic substrates/products either bound or within the milieu of membrane enzymes. In this study, we used styrene maleic acid lipid particle (SMALP) nanodiscs and electrospray ionization‐ion mobility spectrometry–mass spectrometry (ESI‐IMS) mass spectrometry to address the stability of lipid‐bound states of two bacterial β‐barrel enzymes. We overexpressed and purified different oligomeric states of two model β‐barrel enzymes with their co‐associated lipids (i.e. enzyme substrates/products) from the outer membrane of E.coli using various polymer formulations. We further examined the stability of the intact lipid‐protein nanodiscs in solution as well as in the gas phase. Our results, thus far, has demonstrated the higher efficacy of the native nanodiscs over conventional detergents in isolating lipid populations bound to the enzymes, also an improved thermal stability of the resulting β‐barrel complexes in solution. We have, interestingly, observed that some polymers are more compatible with the required conditions in mass spectrometry hence overcoming limitations (e.g. low pH which is known as destabilizing factor for the original SMALP nanodiscs) in the structural analysis of lipid‐bound states of membrane proteins in the gas phase. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".