Effects of hydrophobicity and anions on self‐assembly of the peptide EMK16‐II
Bibliographic record
Abstract
Effects of hydrophobic and electrostatic interactions on the self-assembling process of the ionic-complementary peptide EMK16-II are investigated by atomic force microscopy imaging, circular dichroism spectra, light scattering, and chromatography. It is found that the hydrophobicity of the peptide promotes the aggregation in pure water even at a very low concentration, resulting in a much lower critical aggregation concentration than that of another peptide, EAK16-II. The effect of anions in solution with different valences on electrostatic interactions is also important. Monovalent anions (Cl(-) and Ac(-)) with a proper concentration can facilitate the formation of peptide fibrils, with Cl(-) of smaller size being more effective than Ac(-) of larger size. However, only small amounts of fibrils, but plenty of large amorphous aggregates, are found when the peptide solution is incubated with multivalent anions, such as SO(4)(2-), C(6)H(5)O(7) (3-), and HPO(4)(2-). More importantly, by gel filtration chromatography, the citrate anion, which induces a similar effect on the self-assembling process of EMK16-II as that of SO(4)(2-) and HPO(4)(2-), can interact with two or more positively charged residues of the peptide and reside in the amorphous aggregates. This implies a "salt bridge" effect of multivalent anions on the peptide self-assembling process, which can interpret a previous puzzle why divalent cations inhibit the formation of ordered nanofibrils of the ionic-complementary peptides. Thus, our results clarify the important effects of hydrophobic and electrostatic interactions on the self-assembling process of the ionic-complementary peptides. These are greatly helpful for us to understand the mechanism of peptides' self-assembling process and protein folding and aggregation.
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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".