Three-dimensional structure model and predicted ATP interaction rewiring of a deviant RNA ligase 2
Bibliographic record
Abstract
BACKGROUND: RNA ligases 2 are scarce and scattered across the tree of life. Two members of this family are well studied: the mitochondrial RNA editing ligase from the parasitic trypanosomes (Kinetoplastea), a promising drug target, and bacteriophage T4 RNA ligase 2, a workhorse in molecular biology. Here we report the identification of a divergent RNA ligase 2 (DpRNL) from Diplonema papillatum (Diplonemea), a member of the kinetoplastids' sister group. METHODS: We identified DpRNL with methods based on sensitive hidden Markov Model. Then, using homology modeling and molecular dynamics simulations, we established a three dimensional structure model of DpRNL complexed with ATP and Mg2+. RESULTS: The 3D model of Diplonema was compared with available crystal structures from Trypanosoma brucei, bacteriophage T4, and two archaeans. Interaction of DpRNL with ATP is predicted to involve double π-stacking, which has not been reported before in RNA ligases. This particular contact would shift the orientation of ATP and have considerable consequences on the interaction network of amino acids in the catalytic pocket. We postulate that certain canonical amino acids assume different functional roles in DpRNL compared to structurally homologous residues in other RNA ligases 2, a reassignment indicative of constructive neutral evolution. Finally, both structure comparison and phylogenetic analysis show that DpRNL is not specifically related to RNA ligases from trypanosomes, suggesting a unique adaptation of the latter for RNA editing, after the split of diplonemids and kinetoplastids. CONCLUSION: Homology modeling and molecular dynamics simulations strongly suggest that DpRNL is an RNA ligase 2. The predicted innovative reshaping of DpRNL's catalytic pocket is worthwhile to be tested experimentally.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".