Impact of Differential Detergent Interactions on Transmembrane Helix Dimerization Affinities
Bibliographic record
Abstract
Interactions between transmembrane (TM) helices play a critical role in the fundamental processes required for cells to communicate and exchange materials with their surroundings. Our understanding of the factors that promote TM helix interactions has greatly benefited from our ability to study these interactions in the solution phase through the use of membrane-mimetic micelles. However, less is known about the potential influence of juxtamembrane regions flanking the interacting TM helices that may modulate dimerization affinities, even when the interacting surface itself is not altered. To investigate this question, we used solution NMR to quantitate the dimerization affinity of the major coat protein from the M13 bacteriophage in sodium dodecyl sulfate (SDS), a well-characterized model of a single-spanning self-associating TM protein. Here, we showed that a shorter construct lacking the N-terminal amphipathic helix has a higher dimerization affinity relative to that of the full-length protein, with no change in the helical structure between the monomeric and dimeric states in both cases. Although this translated into a 0.6 kcal/mol difference in free energy when the SDS solvent was approximated as a continuous phase, there were deviations from this model at high protein to detergent ratios. Instead, the equilibria were better fit to a model that treats the empty micelle as an active participant in the reaction, giving rise to standard free energies of association that were the same for both full-length and TM-segment constructs. According to this model, the higher apparent affinity of the shorter peptide could be completely explained by the enhanced detergent binding by the monomer relative to that bound by the dimer. Therefore, differential detergent binding between the monomeric and dimeric states provides a mechanism by which TM helix interactions can be modulated by noninteracting juxtamembrane regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".