Insights into the molecular determinants involved in cap recognition by the vaccinia virus D10 decapping enzyme
Bibliographic record
Abstract
Decapping enzymes are required for the removal of the 5'-end (m7)GpppN cap of mRNAs to allow their decay in cells. While many cap-binding proteins recognize the cap structure via the stacking of the methylated guanosine ring between two aromatic residues, the precise mechanism of cap recognition by decapping enzymes has yet to be determined. In order to get insights into the interaction of decapping enzymes with the cap structure, we studied the vaccinia virus D10 decapping enzyme as a model to investigate the important features for substrate recognition by the enzyme. We demonstrate that a number of chemically modified purines can competitively inhibit the decapping reaction, highlighting the molecular features of the cap structure that are required for recognition by the enzyme, such as the nature of the moiety at positions 2 and 6 of the guanine base. A 3D structural model of the D10 protein was generated which suggests amino acids implicated in cap binding. Consequently, we expressed 17 mutant proteins with amino acid substitutions in the active site of D10 and found that eight are critical for the decapping activity. These data underscore the functional features involved in the non-canonical cap-recognition by the vaccinia virus D10 decapping enzyme.
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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".