Peptide and protein‐based nanotubes for nanobiotechnology
Bibliographic record
Abstract
Abstract The development of biologically relevant nanosystems such as biomolecular probes and sensors requires systems that effectively interface specific biochemical environments with abiotic architectures. The most widely studied nanomaterial, carbon nanotubes, has proven challenging in their adaptation for biomedical applications despite their numerous advantageous physical and electrochemical properties. On the other hand, development of bionanosystems through adaptation of existing biological systems has several advantages including their adaptability through modern recombinant DNA strategies. Indeed, the use of peptides, proteins and protein assemblies as nanotubes, scaffolds, and nanowires has shown much promise as a bottom‐up approach to the development of novel bionanosystems. We highlight several unique peptide and protein systems that generate protein nanotubes (PNTs) that are being explored for the development of biosensors, probes, bionanowires, and drug delivery systems. WIREs Nanomed Nanobiotechnol 2012, 4:575–585. doi: 10.1002/wnan.1180 This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Biology-Inspired Nanomaterials > Peptide-Based Structures
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".