Structural characterization of the model amphipathic peptide Ac-LKKLLKLLKKLLKL-NH<sub>2</sub> in aqueous solution and with 2,2,2-trifluoroethanol and 1,1,1,3,3,3-hexafluoroisopropanol
Bibliographic record
Abstract
Short-chain amphipathic peptides are promising components in the new generation of engineered biomaterials. The model 14-residue leucine–lysine peptide Ac-LKKLLKLLKKLLKL-NH2 (LKα) is one such amphipathic peptide. In dilute aqueous solution (<0.05 mmol/L), it was previously proposed, using CD spectroscopic data, that LKα existed in a cooperative monomeric (unstructured) – tetrameric (α-helical) equilibrium that shifted towards the tetramer at high NaCl and peptide concentrations. Here, at similar peptide concentrations, CD spectroscopy shows that LKα readily adopts α-helical structure in the presence of 2,2,2-trifluoroethanol (TFE) and 1,1,1,3,3,3-hexafluoroisopropanol (HFIP) with maximal helical character in 20% TFE and ∼10% HFIP (v/v). The helical character in fluorinated alcohols suggested by the CD data at low peptide concentrations (0.06 mmol/L) is corroborated at high peptide concentrations (1.5 mmol/L) by NMR NOE data that also show that 1.5 mmol/L LKα is helical in 100% water. Size exclusion chromatography and estimations of rotational correlation times (τc) showed that the self-assembled LKα complexes contained three to five peptides. Removing the N-terminal acetyl group prevents LKα from forming helices and self-associating at high NaCl and peptide concentrations. This more detailed characterization of the structural and physical properties of LKα over a greater range of peptide concentrations and in the presence of fluorinated alcohols will assist the design of biomaterials containing amphipathic peptides and guide the ability to control self-assembly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".