Pi Release Limits the Intrinsic and RNA-Stimulated ATPase Cycles of DEAD-Box Protein 5 (Dbp5)
Bibliographic record
Abstract
mRNA export from the nucleus depends on the ATPase activity of the DEAD-box protein Dbp5/DDX19. Although Dbp5 has measurable ATPase activity alone, several regulatory factors (e.g., RNA, nucleoporin proteins, and the endogenous small molecule InsP6) modulate catalytic activity in vitro and in vivo to facilitate mRNA export. An analysis of the intrinsic and regulator-activated Dbp5 ATPase cycle is necessary to define how these factors control Dbp5 and mRNA export. Here, we report a kinetic and equilibrium analysis of the Saccharomyces cerevisiae Dbp5 ATPase cycle, including the influence of RNA on Dbp5 activity. These data show that ATP binds Dbp5 weakly in rapid equilibrium with a binding affinity (KT~4 mM) comparable to the KM for steady-state cycling, while ADP binds an order of magnitude more tightly (KD~0.4 mM). The overall intrinsic steady-state cycling rate constant (kcat) is limited by slow, near-irreversible ATP hydrolysis and even slower subsequent phosphate release. RNA increases kcat and rate-limiting Pi release 20-fold, although Pi release continues to limit steady-state cycling in the presence of RNA, in conjunction with RNA binding. Together, this work identifies RNA binding and Pi release as important biochemical transitions within the Dbp5 ATPase cycle and provides a framework for investigating the means by which Dbp5 and mRNA export is modulated by regulatory factors.
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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 | 0.001 | 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".