Expanding the DOPA Universe with Genetically Encoded, Mussel‐Inspired Bioadhesives for Material Sciences and Medicine
Bibliographic record
Abstract
Catechols are a biologically relevant group of aromatic diols that have attracted much attention as mediators of adhesion of "bio-glue" proteins in mussels of the genus Mytilus. These organisms use catechols in the form of the noncanonical amino acid l-3,4-dihydroxyphenylalanine (DOPA) as a building block for adhesion proteins. The DOPA is generated post-translationally from tyrosine. Herein, we review the properties, natural occurrence, and reactivity of catechols in the design of bioinspired materials. We also provide a basic description of the mussel's attachment apparatus, the interplay between its different molecules that play a crucial role in adhesion, and the role of post-translational modifications (PTMs) of these proteins. Our focus is on the microbial production of mussel foot proteins with the aid of orthogonal translation systems (OTSs) and the use of genetic code engineering to solve some fundamental problems in the bioproduction of these bioadhesives and to expand their chemical space. The major limitation of bacterial expression systems is their intrinsic inability to introduce PTMs. OTSs have the potential to overcome these challenges by replacing canonical amino acids with noncanonical ones. In this way, PTM steps are circumvented while the genetically programmed precision of protein sequences is preserved. In addition, OTSs should enable spatiotemporal control over the complex adhesion process, because the catechol function can be masked by suitable chemical protection. Such caged residues can then be noninvasively unmasked by, for example, UV irradiation or thermal treatment. All of these features make OTSs based on genetic code engineering in reprogrammed microbial strains new and promising tools in bioinspired materials science.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".