Micelle-Catalyzed Domain Swapping in the GlpG Rhomboid Protease Cytoplasmic Domain
Bibliographic record
Abstract
Three-dimensional domain swapping is a mode of self-interaction that can give rise to altered functional states and has been identified as the trigger event in some protein deposition diseases, yet rates of interconversion between oligomeric states are usually slow, with the requirement for transient disruption of an extensive network of interactions giving rise to a large kinetic barrier. Here we demonstrate that the cytoplasmic domain of the Escherichia coli GlpG rhomboid protease undergoes slow dimerization via domain swapping and that micromolar concentrations of micelles can be used to enhance monomer-dimer exchange rates by more than 1000-fold. Detergents bearing a phosphocholine headgroup are shown to be true catalysts, with hexadecylphosphocholine reducing the 26 kcal/mol free energy barrier by >11 kcal/mol while preserving the 5 kcal/mol difference between monomer and dimer states. Catalysis involves the formation of a micelle-bound intermediate with a partially unfolded structure that is primed for domain swapping. Taken together, these results are the first to demonstrate true catalysis for domain swapping, by using micelles that work in a chaperonin-like fashion to unfold a kinetically trapped state and allow access to the domain-swapped form.
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) | 0.000 | 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".