Loop Sequence Dictates the Secondary Structure of a Human Membrane Protein Hairpin
Bibliographic record
Abstract
Membrane proteins adopt two fundamental types of folds in nature: membranes in all organisms harbor α-helical bundles linked by extramembranous loops of varying length, while β-barrel structures are found in the outer membrane of Gram-negative bacteria, mitochondria, and chloroplasts. Here we report that turn-inducing loop mutations in a transmembrane hairpin induce the conversion of an α-helical hairpin to β-sheet oligomers in membrane environments. On the basis of an observation of a sequence bias toward Pro and Gly in the turns of native β-barrel membrane proteins, we characterized in sodium dodecyl sulfate (SDS) micelles and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers several "hairpin" constructs of cystic fibrosis transmembrane conductance regulator transmembrane segments 3 and 4 (TM3-loop-TM4; loop region being (215)IWELLQASA(223)) in which Pro-Gly residues were either inserted or substituted at several positions. Remarkably, suitable positioning of the Pro-Gly doublet caused the adoption of stable β-sheet structures by several mutants in SDS micelles, as shown by circular dichroism spectroscopy, concurrent with a ladder of discrete oligomers observed via SDS-polyacrylamide gel electrophoresis. Reconstitution of wild-type (WT) TM3/4 into POPC vesicles studied by Trp fluorescence, in conjunction with positional quenchers in brominated phospholipids, indicated a transbilayer position for helical WT TM3/4, but likely a largely surface-embedded conformation for the β-sheet mutant with loop region IWPGELLQASA. To the best of our knowledge, such a complete change in the fold with a minimal number of mutations has not been previously observed for a membrane protein. These facile α-helix to β-sheet conversions highlight the contribution of loops to membrane protein structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".