Distinct Dynamic Modes Enable the Engagement of Dissimilar Ligands in a Promiscuous Atypical RNA Recognition Motif
Bibliographic record
Abstract
Conformational dynamics play a critical role in ligand binding, often conferring divergent activities and specificities even in species with highly similar ground-state structures. Here, we employ time-resolved electrospray ionization hydrogen-deuterium exchange (TRESI-HDX) to characterize the changes in dynamics that accompany oligonucleotide binding in the atypical RNA recognition motif (RRM2) in the C-terminal domain (CTD) of human La protein. Using this approach, which is uniquely capable of probing changes in the structure and dynamics of weakly ordered regions of proteins, we reveal that binding of RRM2 to a model 23-mer single-stranded RNA and binding of RRM2 to structured IRES domain IV of the hepatitis C viral (HCV) RNA are driven by fundamentally different dynamic processes. In particular, binding of the single-stranded RNA induces helical "unwinding" in a region of the CTD previously hypothesized to play an important role in La and La-related protein-associated RNA remodeling, while the same region becomes less dynamic upon engagement with the double-stranded HCV RNA. Binding of double-stranded RNA also involves less penetration into the RRM2 binding pocket and more engagement with the unstructured C-terminus of the La CTD. The complementarity between TRESI-HDX and Δδ nuclear magnetic resonance measurements for ligand binding analysis is also explored.
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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 | 0.000 |
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".