Effect of Amino Acid Sequence and pH on Nanofiber Formation of Self-Assembling Peptides EAK16-II and EAK16-IV
Bibliographic record
Abstract
Atomic force microscopy (AFM) and axisymmetric drop shape analysis-profile (ASDA-P) were used to investigate the mechanism of self-assembly of peptides. The peptides chosen consisted of 16 alternating hydrophobic and hydrophilic amino acids, where the hydrophilic residues possess alternating negative and positive charges. Two types of peptides, AEAEAKAKAEAEAKAK (EAK16-II) and AEAEAEAEAKAKAKAK (EAK16-IV), were investigated in terms of nanostructure formation through self-assembly. The experimental results, which focused on the effects of the amino acid sequence and pH, show that the nanostructures formed by the peptides are dependent on the amino acid sequence and the pH of the solution. For pH conditions around neutrality, one of the peptides used in this study, EAK16-IV, forms globular assemblies and has lower surface tension at air-water interfaces than another peptide, EAK16-II, which forms fibrillar assemblies at the same pH. When the pH is lowered below 6.5 or raised above 7.5, there is a transition from globular to fibrillar structures for EAK16-IV, but EAK16-II does not show any structural transition. Surface tension measurements using ADSA-P showed different surface activities of peptides at air-water interfaces. EAK16-II does not show a significant difference in surface tension for the pH range between 4 and 9. However, EAK16-IV shows a noticeable decrease in surface tension at pH around neutrality, indicating that the formation of globular assemblies is related to the molecular hydrophobicity.
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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".