Membrane protein structural insights from chemical labeling and mass spectrometry
Bibliographic record
Abstract
Membrane proteins play a central role in virtually all biological processes, and they represent important drug targets. Unfortunately, the application of traditional high-resolution methods such as X-ray crystallography and NMR spectroscopy to membrane proteins remains challenging. This article reviews alternative approaches that involve chemical labeling and mass spectrometry (MS) for gaining insights into membrane protein structure, function, and interactions. Hydrogen/deuterium exchange MS represents an interesting avenue for exploring biomolecular conformations and dynamics, but thus far this technique has not been widely adopted for membrane protein studies. The main focus of this article is on the use of labeling agents that introduce covalent modifications in solvent-accessible protein regions. While it is possible to monitor the occurrence of single-site modifications using traditional biochemical methods or optical spectroscopy, the use of MS greatly enhances the scope and potential of this approach because multiple tagging events can be detected in parallel. The traditional bottom-up workflow of these studies involves the digestion of a chemically labeled membrane protein by a specific protease such as trypsin. This is followed by chromatographic separation of the resulting peptides and on-line electrospray ionization MS. The application of tandem MS allows pinpointing the exact locations of chemical modifications. A particularly exciting aspect is the applicability of covalent labeling techniques to membrane protein within their natural lipid environment, or even inside living cells. Some of these concepts will be illustrated using the oxidative labeling of bacteriorhodopsin as an example, but numerous other labeling agents and protein systems are being highlighted as well. It is hoped that this review will stimulate further developments in the characterization of membrane proteins.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".