Sequence Hydropathy Dominates Membrane Protein Response to Detergent Solubilization
Bibliographic record
Abstract
The ability to predict from amino acid sequence how membrane protein structures will respond to detergent solubilization would significantly facilitate experimental characterization of these molecules. Here we have investigated and compared the response to solubilization by the "mild" n-dodecyl-β-D-maltoside (DDM) and "harsh" sodium dodecyl sulfate (SDS) of wild-type and point mutant "hairpin" (helix-loop-helix) membrane proteins derived from the third and fourth TM segments of the human cystic fibrosis transmembrane conductance regulator (CFTR) and the intervening extracellular loop. Circular dichroism spectroscopy, size-exclusion chromatography, and pyrene fluorescence spectroscopy were used to evaluate the secondary structures, hairpin-detergent complex excluded volumes, and hairpin compactness of the detergent-solubilized sequences. Sequence hydrophobicity is found to be the dominant factor dictating membrane protein response to detergent solubilization by DDM and SDS, with hairpin secondary structure exquisitely sensitive to mutation when DDM is used for solubilization. DDM and SDS differ principally in their ability to promote approach of TM segment ends, although hairpin compactness remains sensitive to point mutations. Our overall findings suggest that protein-protein and protein-detergent interactions are determined concomitantly, with the net hydropathy of residues exposed to detergent dominating the observed properties of the solubilized protein.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".