Kinetic characterization of three human DExD/H-box RNA helicases
Bibliographic record
Abstract
Abstract Human DExD/H-box RNA helicases are ubiquitous molecular motors that unwind and rearrange RNA secondary structures in an ATP-dependent manner. These enzymes play essential roles in nearly all aspects of RNA metabolism. While their biological functions are well-characterized, the kinetic mechanisms remain relatively understudied in vitro . In this study, we describe the development and optimization of a bioluminescence-based assay to kinetically characterize three human RNA helicases: MDA5, LGP2, and DDX1. The assays were conducted using annealed 24-mer RNA (blunt-ended double-stranded RNA) or double-stranded RNA (ds-RNA) with a 25- nt 3ʹ overhang. These findings establish a robust and high-throughput in vitro assay suitable for a 384-well format, enabling the discovery and characterization of inhibitors targeting MDA5, LGP2, and DDX1. This work provides a valuable resource for advancing our understanding of these helicases and their therapeutic potential in Alzheimer’s disease.
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 | 0.001 | 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 | 0.001 | 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".