A Structural Analysis of Proteinaceous Nanotube Cavities and Their Applications in Nanotechnology
Bibliographic record
Abstract
Protein nanotubes offer unique properties to the materials science field that allow them to fulfill various functions in drug delivery, biosensors and energy storage. Protein nanotubes are chemically diverse, modular, biodegradable and nontoxic. Furthermore, although the initial design or repurposing of such nanotubes is highly complex, the field has matured to understand underlying chemical and physical properties to a point where applications are successfully being developed. An important feature of a nanotube is its ability to bind ligands via its internal cavities. As ligands of interest vary in size, shape and chemical properties, cavities have to be able to accommodate very specific features. As such, understanding cavities on a structural level is essential for their effective application. The objective of this review is to present the chemical and physical diversity of protein nanotube cavities and highlight their potential applications in materials science, specifically in biotechnology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".