ARiBo pull-down for riboproteomic studies based on label-free quantitative mass spectrometry
Bibliographic record
Abstract
As part of their normal life cycle, most RNA molecules associate with several proteins that direct their fate and regulate their function. Here, we describe a novel method for identifying proteins that associate with a target RNA. The procedure is based on the ARiBo method for affinity purification of RNA, which was originally developed to quickly purify RNA with high yields and purity under native conditions. The ARiBo method was further optimized using in vitro transcribed RNA to capture RNA-associating proteins from cellular extracts with high yields and low background protein contamination. For these RNA pull-downs, stem-loops present in the immature forms of let-7 miRNAs (miRNA stem-loops) were used as the target RNAs. Label-free quantitative mass spectrometry analysis allowed for the reliable identification of proteins that are specific to the stem-loops present in the immature forms of two miRNAs, let-7a-1 and let-7g. Several proteins known to bind immature forms of these let-7 miRNAs were identified, but with an improved coverage compared to previous studies. In addition, several novel proteins were identified that better define the protein interactome of the let-7 miRNA stem-loops and further link let-7 biogenesis to important biological processes such as development and tumorigenesis. Thus, combining the ARiBo pull-down method with label-free quantitative mass spectrometry provides an effective proteomic approach for identification of proteins that associate with a target RNA.
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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 | 0.001 |
| 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) | 0.000 | 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".