Mapping interactions between the RNA chaperone FinO and its RNA targets
Bibliographic record
Abstract
Bacterial conjugation is regulated by two-component repression comprising the antisense RNA FinP, and its protein co-factor FinO. FinO mediates base-pairing of FinP to the 5'-untranslated region (UTR) of traJ mRNA, which leads to translational inhibition of the transcriptional activator TraJ and subsequent down regulation of conjugation genes. Yet, little is known about how FinO binds to its RNA targets or how this interaction facilitates FinP and traJ mRNA pairing. Here, we use solution methods to determine how FinO binds specifically to its minimal high affinity target, FinP stem-loop II (SLII), and its complement SLIIc from traJ mRNA. Ribonuclease footprinting reveals that FinO contacts the base of the stem and the 3' single-stranded tails of these RNAs. The phosphorylation or oxidation of the 3'-nucleotide blocks FinO binding, suggesting FinO binds the 3'-hydroxyl of its RNA targets. The collective results allow the generation of an energy-minimized model of the FinO-SLII complex, consistent with small-angle X-ray scattering data. The repression complex model was constrained using previously reported cross-linking data and newly developed footprinting results. Together, these data lead us to propose a model of how FinO mediates FinP/traJ mRNA pairing to down regulate bacterial conjugation.
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 | 0.000 | 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 | 0.000 |
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 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".