Current advances in Phi29 pRNA biology and its application in drug delivery
Bibliographic record
Abstract
Abstract Bacteriophage 29 (Phi29) packaging RNA (pRNA) is one of the key components in the viral DNA‐packaging motor. It contains two functional domains facilitating the translocation of DNA into the viral capsid by interacting with other elements in the motor and promoting adenosine triphosphates hydrolysis. Through the connection between interlocking loops in adjacent pRNA monomers, pRNA functions in the form of multimer ring in the motor. Previous studies have addressed the unique structure and conformation of pRNA. However, there are different DNA‐packaging models proposed for the viral genome transportation mechanism. The DNA‐packaging ability and the unique features of pRNA have been attracting efforts to study its potential applications in nanotechnology. The pRNA has been proved to be a promising tool for delivering nucleic acid‐based therapeutic molecules by covalent linkage with ribozymes, small interfering RNAs, aptamers, and artificial microRNAs. The flexibility in constructing dimers, trimers, and hexamers enables the assembly of polyvalent nanoparticles to carry drug molecules for therapeutic purposes, cell ligands for target delivery, image detector for drug entry monitoring, and endosome disrupter for drug release. Besides these fascinating pharmacological advantages, pRNA‐based drug delivery has also been demonstrated to prolong the drug half life with minimal induction of immune response and toxicity. WIREs RNA 2012, 3:469–481. doi: 10.1002/wrna.1111 This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems RNA Processing > RNA Editing and Modification RNA in Disease and Development > RNA in Disease
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".