CRITICAL SELF-ASSEMBLY CONCENTRATION OF AN IONIC-COMPLEMENTARY PEPTIDE EAK16-I
Bibliographic record
Abstract
Understanding the process of self-assembly of peptides has been important in various biomedical engineering applications. This work focuses on the effect of peptide concentration on the molecular self-assembly of an ionic-complementary peptide, EAK16-I (AEAKAEAKAEAKAEAK), in aqueous solution. The surface tension and self-assembled nanostructures were determined for a wide range of peptide concentrations using axisymmetric drop shape analysis-profile (ADSA–P) and atomic force microscopy (AFM), respectively. Surface tension measurements revealed a critical self-assembly concentration of 0.3 mg peptide/ml water, below which the surface tension decreased rapidly with increasing peptide concentration, and above which the surface tension remained at a constant, plateau value. There were two structural transitions observed with increasing peptide concentration: the first was from globular nanostructures to fibrils, and the second from the fibrils to relatively thick fibers. The second structural transition occurred at the critical self-assembly concentration as determined by the surface tension measurements. The nanostructural behavior of EAK16-I was compared with that of EAK16-II, which has the same amino acid composition but a different charge distribution. Salt effects were also examined by adding NaCl to the peptide solution. The salt addition facilitated the formation of peptide fibrils at low peptide concentrations but increased the critical self-assembly concentration, which occurred at 0.8 mg peptide/ml water in the presence of 20 mM NaCl. The structural transitions involved in the self-assembly of EAK16-I resemble those from protofibrils to fibrils observed with numerous naturally occurring peptides. An understanding of this structural transition may have relevance in the analysis and treatment of peptide/protein conformational diseases and have application in the production of self-assembled protein nanostructures.
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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".