Side Chain Effect on Ion Channel Characters of Aib Rich Peptides
Bibliographic record
Abstract
As models of ion channel proteins and naturally occurring pore-forming peptides, we designed a series of Aib rich peptides [Ac-(Aib-Xxx-Aib-Ala)(5)-NH(2) (Xxx = Lys, Glu, Ser, and Gly: BXBA-20)] to investigate the effects of the side chains of the amino acid residues Lys, Glu, Ser, and Gly on the conformation and electrophysiological properties of ion channels. The conformation of peptides and their affinity for phospholipid membranes were evaluated by CD spectroscopy. Patch-clamp experiments revealed that all BXBA-20 peptides form ion channels in DPhPC bilayers exhibiting clearly resolved transitions between the open and closed states. The channel forming frequency was in the order BKBA-20>BEBA-20>BSBA-20>BGBA-20. In the case of BKBA-20 and BEBA-20, the self-assembled conductive oligomers expressed homogeneous and voltage-independent single channel conductances. In contrast, heterogeneous conductance was observed in BSBA-20 and BGBA-20 ion channels under similar experimental conditions. From these results, we conclude that peptides with a high degree of helical conformation, high amphipathicity, high affinity for lipid membranes, and self-associating characters in vesicles are most suitable for inducing ion channels with a high frequency of occurrence. Moreover, BEBA-20, BSBA-20, and BGBA-20 channels were cation-selective, whereas the BKBA-20 channel was non-selective.
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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 | 0.000 | 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".