Characterising protein/detergent complexes by triple-detection size-exclusion chromatography
Bibliographic record
Abstract
BACKGROUND: In vitro investigations of membrane proteins usually depend on detergents for protein solubilisation and stabilisation. The amount of detergent bound to a membrane protein is relevant to successful experiment design and data analysis but is often unknown. Triple-detection size-exclusion chromatography enables simultaneous separation of protein/detergent complexes and protein-free detergent micelles and determination of their molar masses in a straightforward and absolute manner. Size-exclusion chromatography is used to separate different species, while ultraviolet absorbance, static light scattering, and refractive index measurements allow molar mass determination of protein and detergent components. RESULTS: We refined standard experimental and data-analysis procedures for challenging membrane-protein samples that elude routine approaches. The general procedures including preparatory steps, measurements, and data analysis for the characterisation of both routine and complex samples in difficult solvents such as concentrated denaturant solutions are demonstrated. The applicability of the protocol but also its limitations and possible solutions are discussed, and an extensive troubleshooting section is provided. CONCLUSIONS: We established and validated a protocol for triple-detection size-exclusion chromatography that enables the inexperienced user to perform and analyse measurements of well-behaved protein/detergent complexes. More experienced users are provided with an example of a more sophisticated analysis procedure allowing mass determination under challenging separation conditions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".