Molecular Engineering of E. coli Bacterioferritin: A Versatile Nanodimensional Protein Cage
Bibliographic record
Abstract
Currently, intense interest is focused on the discovery and application of new multisubunit cage proteins and spherical virus capsids to the fields of bionanotechnology, drug delivery, and diagnostic imaging as their internal cavities can serve as hosts for fluorophores or bioactive molecular cargo. Bacterioferritin is unusual in the ferritin protein superfamily of iron-storage cage proteins in that it contains twelve heme cofactors and is homomeric. The goal of the present study is to expand the capabilities of ferritins by developing new approaches to molecular cargo encapsulation employing bacterioferritin. Two strategies were explored to control the encapsulation of a diverse range of molecular guests compared to random entrapment, a predominant strategy employed in this area. The first was the inclusion of histidine-tag peptide fusion sequences within the internal cavity of bacterioferritin. This approach allowed for the successful and controlled encapsulation of a fluorescent dye, a protein (fluorescently labeled streptavidin), or a 5 nm gold nanoparticle. The second strategy, termed the heme-dependent cassette strategy, involved the substitution of the native heme with heme analogs attached to (i) fluorescent dyes or (ii) nickel-nitrilotriacetate (NTA) groups (which allowed for controllable encapsulation of a histidine-tagged green fluorescent protein). An in silico docking approach identified several small molecules able to replace the heme and capable of controlling the quaternary structure of the protein. A transglutaminase-based chemoenzymatic approach to surface modification of this cage protein was also accomplished, allowing for future nanoparticle targeting. This research presents novel strategies to control a diverse set of molecular encapsulations and adds a further level of sophistication to internal protein cavity engineering.
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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".