RNA aptamers as tools for the purification and analysis of in vivo assembled ribonucleoproteins
Bibliographic record
Abstract
A large number of ribonucleoprotein (RNP) complexes are being discovered mediating numerous cellular functions. To investigate the composition, structure, and functional mechanism of RNP complexes, it is advantageous to isolate an RNP that was assembled in vivo. This review provides a systematic overview of a versatile and highly effective method to accomplish this task, namely, the purification of RNPs from cells using genetically encoded RNA aptamers. Inserting an RNA aptamer into the RNA of an RNP enables binding of the tagged RNP with high affinity and specificity to a ligand as an effective affinity chromatography purification strategy. Therefore, the purification of RNPs using aptamers has been used successfully to identify heterogenous populations of RNPs forming around a single RNA as well as to characterize intermediates in the formation of complex RNPs such as the ribosome. Here, we discuss in detail the selection of an appropriate RNA aptamer based on the properties of both the aptamer and its ligand, and we describe critical considerations in designing RNP purifications.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".