Exploring membrane protein structural features by oxidative labeling and mass spectrometry
Bibliographic record
Abstract
Despite their biological importance, the structural characterization of integral membrane proteins (IMPs) by x-ray crystallography and NMR spectroscopy remains challenging. Hence, there is a need for complementary approaches that are capable of probing IMP conformational features in a robust fashion. Covalent labeling relies on the principle that solvent accessible regions can be modified by reactive species, whereas buried segments are protected. The readout of the labeling pattern is conducted by mass spectrometry. Hydroxyl radical (·OH) introduces oxidative modifications at amino acid side chains. In this article, the authors discuss the application of ·OH labeling for the structural interrogation of IMPs. Kyte-Doolittle hydropathy analyses are widely used for generating IMP topology models. The validation of these models by mutational techniques is labor intensive. ·OH labeling can readily distinguish transmembrane elements from solvent-exposed loops, thereby providing an alternative topology validation tool. For IMPs with published crystal structures, oxidative modifications can report on functionally relevant dynamic features that are invisible in the static x-ray data. The coupling of pulsed ·OH labeling with rapid mixing techniques represents a novel approach for studying IMP folding kinetics. In conclusion, ·OH labeling is a versatile tool that can provide insights into the structure and dynamics of IMPs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".