Anion Effect on the Nanostructure of a Metal Ion Binding Self-Assembling Peptide
Bibliographic record
Abstract
Effects of copper salts containing different anions (SO(4)(2)(-), Cl(-), and NO(3)(-)) on the self-assembly of a designed peptide EAK16(II)GGH with affinity for Cu(2+) have been investigated. The peptide secondary structure, self-assembled nanostructures, and surface activity were observed to depend strongly on the type of anion. Over a salt concentration range from 0.05 to 10.0 mM, SO(4)(2)(-) induced long fiber formation, whereas Cl(-) and NO(3)(-) caused short fiber formation. The fiber length increased with copper sulfate concentration, but the concentration of copper chloride and copper nitrate did not affect the peptide nanostructures significantly. Analysis by Fourier transform infrared spectroscopy (FTIR) revealed that the addition of the copper salts tended to cause the peptide conformation to change from alpha-helix/random coil to beta-sheet, the extent to which depended on the anion type. This evidence of the anion effect was also supported by surface tension measurements using the axisymmetric drop shape analysis-profile (ADSA-P) technique. An explanation for the effect of anions on the peptide self-assembly was proposed. The divalent anion SO(4)(2)(-) might serve as a bridge by electrostatically interacting with two lysine residues from different peptide molecules, promoting beta-sheet formation. The extensive beta-sheet formation may further promote peptide self-assembly into long fibers. On the other hand, monovalent anions Cl(-) and NO(3)(-) may only electrostatically interact with one charged residue of the peptide; hence, a mixed secondary structure of alpha-helix/random coil and beta-sheet was observed. This observation might explain the predominant formation of short fibers in copper chloride and copper nitrate solutions.
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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 | 0.001 | 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".