QM/MM investigation of the discriminatory pre-transfer editing mechanism operated by Lysyl-tRNA synthetase
Bibliographic record
Abstract
Aminoacyl-tRNA synthetases (aaRSs) are essential enzymes that remarkable facilitate the aminoacylation process during translation. With a high fidelity, the mischarged tRNA is prevented through implementing pre- and post-transfer proofreading mechanisms. For instance, Lysine-tRNA synthetase charges the native substrate, lysine, to its cognate tRNA. In spite of the great structural similarity between lysine to the noncognate and toxic ornithine, with the side chain of lysine being only one methylene group longer, LysRS is able to achieve this discrimination with a high efficiency. In this work, the hybrid quantum mechanics/molecular mechanics (QM/MM) investigation was applied to probe the pre-transfer editing mechanism catalyzed by lysyl-tRNA synthetase to reject the noncognte aminoacyl, L-ornityl (Orn), compared to the cognate substrate, L-lysyl. Particularly, the self-cyclization pre-transfer editing mechanism was explored for the two substrates. The substrate-assisted self-cyclization editing of Orn-AMP, where its phosphate moiety acts as the catalytic base, is found to be the rate-determining step with an energy barrier of 101.2 kJ mol−1. Meanwhile, the corresponding rate-limiting pathway for the native Lys-AMP lies at 140.2 kJ mol−1. This observation clearly indicated the infeasibility of this catalytic scenario in the presence of the native substrate. Interestingly, a thermodynamically favorable cyclic product of −92.9 kJ mol−1 with respect to the aminoacyl reactant complex demonstrated evidence of a successful pre-transfer editing. This reaction resulted in the discharge of the on-cognate -ornithine derivative from LysU’s active site. These valuable mechanistic insights are valuable to enrich our knowledge of this extremely efficient and specific catalytic machinery of LysRS.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".