Catechol-Vanadium Binding Enhances Cross-Linking and Mechanics of a Mussel Byssus Coating Protein
Bibliographic record
Abstract
Inspired largely by chemistry discovered in the byssus fibers of marine mussels, 3,4-dihydroxyphenylalanine (DOPA)–metal coordination has emerged as an effective cross-linking strategy for producing a range of high-performance supramolecular polymers and adhesives with desirable mechanical properties. Recent findings have revealed that mussels often use vanadium in these complexes to enhance the cohesion, rather than solely iron as conventionally believed. Here, we utilized a recombinant DOPA-functionalized mussel protein (rMfp-1-DOPA) to investigate the implications of vanadium-based cross-linking using both nanomechanical and spectroscopic methods. We discovered that DOPA–V cross-linking can strengthen the cohesion of the protein network by nearly twofold compared with DOPA–Fe. Spectroscopic analysis with UV–vis, Raman, and EPR indicated that vanadium is more effective at forming tris-DOPA–metal complexes under the pH ranges relevant to byssus formation and function, especially when starting with the V III form. Furthermore, when compared with a pure catechol control, rMfp-1-DOPA can form DOPA–V complexes at very low pH values relevant to the byssus formation process, suggesting that the protein sequence may be adapted for V binding. These findings provide evidence that DOPA–V coordination is advantageous for both the formation and function of the byssus, which is highly relevant for continued efforts to develop mussel-inspired metallopolymers.
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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".