Biomimetic engineering of conductive curli protein films
Bibliographic record
Abstract
Bioelectronic systems derived from peptides and proteins are of particular interest for fabricating novel flexible, biocompatible and bioactive devices. These synthetic or recombinant systems designed for mediating electron transport often mimic the proteinaceous appendages of naturally occurring electroactive bacteria. Drawing inspiration from such conductive proteins with a high content of aromatic residues, we have engineered a fibrous protein scaffold, curli fibers produced by Escherichia coli bacteria, to enable long-range electron transport. We report the genetic engineering and characterization of curli fibers containing aromatic residues of different nature, with defined spatial positioning, and with varying content on single self-assembling CsgA curli subunits. Our results demonstrate the impressive versatility of the CsgA protein for genetically engineering protein-based materials with new functions. Through a scalable purification process, we show that macroscopic gels and films can be produced, with engineered thin films exhibiting a greater conductivity compared with wild-type curli films. We anticipate that this engineered conductive scaffold, and our approach that combines computational modeling, protein engineering, and biosynthetic manufacture will contribute to the improvement of a range of useful bio-hybrid technologies.
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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".