A rapid method for assessing lipid:protein and detergent:protein ratios in membrane-protein crystallization
Bibliographic record
Abstract
A simple procedure for rapidly measuring lipid:protein ratios and detergent concentrations at different stages of the solubilization, purification and crystallization of membrane proteins has been developed. Fourier-transform infrared spectra recorded from 10 micro l aliquots of solution using a single-bounce diamond-attenuated total reflectance apparatus exhibit characteristic bands arising from the vibrations of lipid, protein and detergent. Lipid:protein molar ratios as low as 5:1 (for a protein with a molecular weight of 300 kDa) are determined by comparing the ratio of the integrated intensity of the lipid ester carbonyl band near 1740 cm(-1) with the protein amide I band near 1650 cm(-1). Detergent concentrations at levels well below the critical micellar concentration of most detergents are determined by comparing the integrated intensities of the detergent vibrations, particularly in the 1200-1000 cm(-1) region, with a standard curve. Protein amide I band-shape analysis provides insight into the effects of detergents on protein secondary structure. The importance of monitoring detergent concentration changes during simple procedures, such as the concentration of a membrane protein by ultrafiltration, is demonstrated. This analytical tool has been used to rapidly establish protocols for minimizing lipid and detergent levels in solubilized membrane-protein samples.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".