The Curvature Sensitivity of a Membrane-Binding Amphipathic Helix Can Be Modulated by the Charge on a Flanking Region
Bibliographic record
Abstract
Membrane-induced amphipathic helices (m-AH) can act as membrane curvature sensors by binding preferentially to hydrophobic lipid packing defects enriched in curved surfaces. Reliance on hydrophobicity and membrane curvature for binding is enhanced when electrostatic interactions are weak. We probed the role of modifying membrane and protein charge on the curvature sensing of two m-AH-containing proteins, CTP:phosphocholine cytidylyltransferase (CCT) and α-synuclein (α-syn). The m-AH domains in both proteins are flanked by disordered tails with multiple phosphoserines (CCT) or acidic residues (α-syn), which we mutated to glutamate or serine to modify protein charge. Analysis of binding to vesicles of varying curvature showed that increasing the negative charge of the tail region decreased the binding strength and augmented the curvature dependence, especially for CCT. We attribute this to charge repulsion. Conversely, increasing the membrane negative charge dampened the curvature dependence. Our data suggest that discrimination of curved versus flat membranes with high negative charge could be modulated by phosphorylation.
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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".