Ribonucleoprotein purification and characterization using RNA Mango
Bibliographic record
Abstract
The characterization of RNA–protein complexes (RNPs) is a difficult but increasingly important problem in modern biology. By combining the compact RNA Mango aptamer with a fluorogenic thiazole orange desthiobiotin (TO1-Dtb or TO3-Dtb) ligand, we have created an RNA tagging system that simplifies the purification and subsequent characterization of endogenous RNPs. Mango-tagged RNP complexes can be immobilized on a streptavidin solid support and recovered in their native state by the addition of free biotin. Furthermore, Mango-based RNP purification can be adapted to different scales of RNP isolation ranging from pull-down assays to the isolation of large amounts of biochemically defined cellular RNPs. We have incorporated the Mango aptamer into the S. cerevisiae U1 small nuclear RNA (snRNA), shown that the Mango-snRNA is functional in cells, and used the aptamer to pull down a U1 snRNA-associated protein. To demonstrate large-scale isolation of RNPs, we purified and characterized bacterial RNA polymerase holoenzyme (HE) in complex with a Mango-containing 6S RNA. We were able to use the combination of a red-shifted TO3-Dtb ligand and eGFP-tagged HE to follow the binding and release of the 6S RNA by two-color native gel analysis as well as by single-molecule fluorescence cross-correlation spectroscopy. Together these experiments demonstrate how the Mango aptamer in conjunction with simple derivatives of its flurophore ligands enables the purification and characterization of endogenous cellular RNPs in vitro.
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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) | 0.001 | 0.001 |
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".