Ion-Channel Formation Assisted by Electrostatic Interhelical Interactions in Covalently Dimerized Amphiphilic Helical Peptides
Bibliographic record
Abstract
An ultimate goal of synthetic ion-channel peptide design is to construct stable and functional ion-conducting pores. It is expected that specific interhelical interactions would facilitate the association of helices in phospholipid membranes and the successive helix-bundle formation. In the present study, we rationally designed helix-bundle ion channels using the synthetic hybrid peptide K20E20, a disulfide dimer of cationic- and anionic-amphiphilic helices Ac-CGG-(BKBA) 5-NH 2 and Ac-CGG-(BEBA) 5-NH 2. Circular dichroism (CD) measurements in aqueous media implied helix stabilization in the peptide caused by the interhelical electrostatic interactions. In addition, CD spectra recorded in the presence of DPPC liposomes and dye-leakage measurements suggested a high degree of association of peptide monomers in phospholipid membranes as well as high affinities between peptide and lipid bilayers. These features allowed ion-channel formation at extremely low peptide concentrations (as low as 1 nM). According to electrophysiological analyses, stable helix bundles were constructed of six peptide helices by association of three K20E20 molecules. Helix-helix association in lipid membranes, peptide-membrane interactions, and ion-channel formation of K20E20 peptides were all facilitated by intramolecular electrostatic interactions between the helices of the hybrid peptide and were pH-dependent. Conductance through K20E20 ion channels decreased under acidic conditions because of the interruption of the salt bridges.
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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) | 0.000 | 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".