Proteins, Processing, and Properties of Adhesive Fluid Condensates Purified from Mussels
Bibliographic record
Abstract
Abstract Humans struggle to design effective underwater adhesives, yet they are essential for numerous technical and biomedical applications. In contrast, biological organisms—most notably mussels—have evolved glues that excel in aquatic environments. While researchers have drawn inspiration from mussels, the resulting materials remain limited by a poor understanding of the native mussel glue formation process. Here, the contents of glue secretory vesicles extracted from mussels are investigated, revealing fluid condensates comprised of the various protein components of the glue. Proteomic analysis confirms the presence of several previously unconfirmed glue proteins in the vesicles, as well as several enzymatic components that may play a role in regulating glue oxidation and cross‐linking. Mimicking vesicle conditions, a method is developed to maintain the vesicle proteins in a reconstituted bulk fluid condensate, enabling in vitro analysis and hypothesis testing. A combination of proteomics, vibrational spectroscopy, and nanomechanical adhesion testing reveals the crucial contributions of several physicochemical factors (e.g., pH, sulfate and vanadium ions, sulfhydryls) for the processing and performance of mussel glues as they transition from fluid condensates to microporous solid glues. These findings are crucial for understanding biological glues and the development of next generation bio‐inspired 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) | 0.000 | 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".