Ternary Synergy of Lys, Dopa, and Phe Results in Strong Cohesion of Peptide Films
Bibliographic record
Abstract
Synergistic interactions between 3,4-dihydroxyphenylalanine (Dopa, Y*), cationic residues, and the aromatic rings have been recently highlighted as influential factors that enhance the underwater adhesion strength of mussel foot proteins and their derivatives. In this study, we report the first ever evidence of a cation–catechol–benzene ternary synergy between Y*, lysine (Lys, K), and phenylalanine (Phe, F) in adhesive peptides. We synthesized three hexapeptides containing a different combination of Y*, K, and F, i.e., (KY*) 3, (KF) 3, and (KY*F) 2, respectively, exploring the relationship between the cohesive performance and molecular architecture of peptides. The peptide with the (KY*F) 2 sequence displays the strongest underwater cohesion energy of 10.3 ± 0.3 mJ m –2 from direct nanoscale surface force measurements. Combined with molecular dynamics simulation, we demonstrated that there are more bonding interactions (including cation-π, π–π, and hydrogen bond interactions) in (KY*F) 2 compared to the other two peptides. In addition, peptide (KY*F) 2 still shows the strongest cohesive energies of 7.6 ± 0.7 and 3.7 ± 0.5 mJ m –2 in acidic and high-ionic strength environments, respectively, although the cohesive energy decreases compared to the value in pure water. Our results further explain the underwater cohesion mechanisms combining multiple interactions and offer insights on designing Dopa containing underwater adhesives.
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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".