Fundamentals behind the specificity of <scp>Cysteinyl‐tRNA</scp> synthetase: <scp>MD</scp> and <scp>QM</scp> / <scp>MM</scp> joint investigations
Bibliographic record
Abstract
Abstract Cysteinyl‐tRNA synthetase (CysRS) catalyzes the aminoacylation reaction of cysteine to its cognate tRNA Cys in the first step of protein translation. It is found that CysRS is different from other aaRSs as it transfers cysteine without the need for an editing reaction, which is not applicable in the case of serine despite the similarity in their structures. Surprisingly, the reasons why CysRS has high amino acid specificity are not clear yet. In this research, the binding configurations of Cys‐AMP and its near‐cognate amino acid Ser‐AMP with CysRS are compared by Molecular Dynamics (MD). The results reveal that CysRS screens the substrate Cys‐AMP to a certain extent in the process of combination and recognition, thus providing a guarantee for the high selectivity of the next reaction. While Ser‐AMP is in a folded state in CysRS. In the meanwhile, the interaction between Cys‐AMP and Zn963 in CysRS is much stronger than Ser‐AMP. The substrate‐assisted aminoacylation mechanism in CysRS is also explored by Quantum Mechanics/Molecular Mechanics (QM/MM) modeling. According to the QM/MM potential energies, the energy barrier of TS Cys‐AMP is 91.75 kJ/mol, while that of TS Ser‐AMP is close to 150 kJ/mol. Based on thermochemistry calculations, it is found that the product of Cys‐AMP is more stable than the reactant. In contrast, Ser‐AMP has a reactant that is more stable than its product. As a result, it reflects that the specificity of CysRS originates from both the kinetic and thermodynamical perspectives of the reaction. Our investigations demonstrate comprehensively on how CysRS recognizes and catalyzes the substrate Cys‐AMP, hoping to provide some guidance for researchers in this area.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".