Histidine-rich coiled-coils promote zinc-dependent self-assembly and curing of porous mussel glues
Bibliographic record
Abstract
Challenging to engineer in synthetic glues, wet adhesion is critical for many technical and biomedical applications. Mussels, however, have evolved underwater glues that adhere effectively onto slippery seashore surfaces. Past research on mussel adhesion highlights the importance of the post-translationally modified amino acid 3,4-dihydroxyphenylalanine (DOPA), found in abundance in mussel glue proteins. Yet, DOPA alone is insufficient to match native adhesion in synthetic mimics. Here, we provide evidence that a previously uncharacterized histidine-rich protein (mefp-12) plays a crucial role in the formation, curing, and performance of mussel glue. Biochemical analysis localizes mefp-12 within vesicles of the mussel glue secretory glands, while AI-assisted modeling of its sequence predicts Zn-stabilized coiled coil conformation and several domains resembling zinc-finger motifs. In vitro investigation of a His-rich 𝛼-helical peptide from mefp-12 shows Zn- and pH-dependent liquid-liquid phase separation (LLPS), coalescence, and spreading over the substrate. Exposure to seawater pH induces subsequent self-organization of the fluid condensates into solid nanoporous networks resembling the structure of the native mussel glue. Based on these findings we gain a deeper mechanistic understanding of mussel glue formation and function that challenges the dominant DOPA-centric paradigm, providing new inspiration for design of bio-inspired wet 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".