Nucleotide analogs and molecular modeling studies reveal key interactions involved in substrate recognition by the yeast RNA triphosphatase
Bibliographic record
Abstract
RNA triphosphatases (RTPases) are involved in the addition of the distinctive cap structure found at the 5' ends of eukaryotic mRNAs. Fungi, protozoa and some DNA viruses possess an RTPase that belongs to the triphosphate tunnel metalloenzyme family of enzymes that can also hydrolyze nucleoside triphosphates. Previous crystallization studies revealed that the phosphohydrolase catalytic core is located in a hydrophilic tunnel composed of antiparallel beta-strands. However, all past efforts to obtain structural information on the interaction between RTPases and their substrates were unsuccessful. In the present study, we used computational molecular docking to model the binding of a nucleotide substrate into the yeast RTPase active site. In order to confirm the docking model and to gain additional insights into the molecular determinants involved in substrate recognition, we also evaluated both the phosphohydrolysis and the inhibitory potential of an important number of nucleotide analogs. Our study highlights the importance of specific amino acids for the binding of the sugar, base and triphosphate moieties of the nucleotide substrate, and reveals both the structural flexibility and complexity of the active site. These data illustrate the functional features required for the interaction of an RTPase with a ligand and pave the way to the use of nucleotide analogs as potential inhibitors of RTPases of pathogenic importance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".