The role of extra-membranous inter-helical loops in helix–helix interactions
Bibliographic record
Abstract
The effect of a short loop connecting two transmembrane alpha-helices was studied using molecular dynamics simulations. Helices F and G from bacteriorhodopsin and two corresponding polyalanine helices were embedded in octane and POPC membranes in a transmembrane configuration both with and without the inter-helical loop. The results indicate that the membrane environment and the sequence of the loop are more influential on the dynamics and structure of the motif than the presence of a loop as such, at least for the time-scales investigated. The four residues in the FG loop are stabilized by four hydrogen bonds. These hydrogen bonds are not present in the polyalanine loop, causing it to be more flexible than the FG loop. This effect was observed independently of the protein environment, stressing the importance of the sequence. The structural analysis indicates that the loop has weak stabilizing properties in all environments. The stabilization due to the presence of the loop was strongest in a simulation of the FG fragment in a membrane-mimetic octane slab. In the simulations of the helix-loop-helix motif embedded in an explicit lipid bilayer model, the lipid bilayer interface compensates to a large extent for the absence of the loop.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".