Deciphering the molecular basis for nucleotide selection by the West Nile virus RNA helicase
Bibliographic record
Abstract
The West Nile virus RNA helicase uses the energy derived from the hydrolysis of nucleotides to separate complementary strands of RNA. Although this enzyme has a preference for ATP, the bias towards this purine nucleotide cannot be explained on the basis of specific protein-ATP interactions. Moreover, the enzyme does not harbor the characteristic Q-motif found in other helicases that regulates binding to ATP. In the present study, we used structural homology modeling to generate a model of the West Nile virus RNA helicase active site that provides instructive findings on the interaction between specific amino acids and the ATP substrate. In addition, we evaluated both the phosphohydrolysis and the inhibitory potential of a collection of 30 synthetic purine analogs. A structure-guided alanine scan of 16 different amino acids was also performed to clarify the contacts that are made between the enzyme and ATP. Our study provides a molecular rationale for the bias of the enzyme for ATP by highlighting the specific functional groups on ATP that are important for binding. Moreover, we identified three new essential amino acids (Arg-185, Arg-202 and Asn-417) that are critical for phosphohydrolysis. Finally, we provide evidence that a region located upstream of motif I, which we termed the nucleotide specificity region, plays a functional role in nucleotide selection which is reminiscent to the role exerted by the Q-motif found in other helicases.
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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) | 0.000 | 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 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".