Strong reversible Fe <sup>3+</sup> -mediated bridging between dopa-containing protein films in water
Bibliographic record
Abstract
Metal-containing polymer networks are widespread in biology, particularly for load-bearing exoskeletal biomaterials. Mytilus byssal cuticle is an especially interesting case containing moderate levels of Fe(3+) and cuticle protein-mussel foot protein-1 (mfp-1), which has a peculiar combination of high hardness and high extensibility. Mfp-1, containing 13 mol % of dopa (3, 4-dihydroxyphenylalanine) side-chains, is highly positively charged polyelectrolyte (pI approximately 10) and didn't show any cohesive tendencies in previous surface forces apparatus (SFA) studies. Here, we show that Fe(3+) ions can mediate unusually strong interactions between the positively charged proteins. Using an SFA, Fe(3+) was observed to impart robust bridging (W(ad) approximately 4.3 mJ/m(2)) between two noninteracting mfp-1 films in aqueous buffer approaching the ionic strength of seawater. The Fe(3+) bridging between the mfp-1-coated surfaces is fully reversible in water, increasing with contact time and iron concentration up to 10 microM; at 100 microM, Fe(3+) bridging adhesion is abolished. Bridging is apparently due to the formation of multivalent dopa-iron complexes. Similar Fe-mediated bridging (W(ad) approximately 5.7 mJ/m(2)) by a smaller recombinant dopa-containing analogue indicates that bridging is largely independent of molecular weight and posttranslational modifications other than dopa. The results suggest that dopa-metal interactions may provide an energetic new paradigm for engineering strong, self-healing interactions between polymers under water.
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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".