A molecular dynamics simulation investigation on the effects of Ser/Cys exchange in the Ser–<i>cis</i>Ser–Lys catalytic triad of malonamidase E2
Bibliographic record
Abstract
The enzyme malonamidase E2 catalytically hydrolyzes malonamate (MLA) into malonate and ammonia. Its active site contains an uncommon Ser– cisSer–Lys catalytic triad that is critical to its functioning. Mutations of the residues in this triad can not only provide insight into its key features but also potentially identify how it may be influenced or adapted, i.e., decreased or increased. In this study, the effects of single and double Ser/Cys exchanges on the wild-type Ser155– cisSer131–Lys62 catalytic triad (i.e., Cys155– cisSer131–Lys62 (S155C), Ser155– cisCys131–Lys62 (S131C), and Cys155– cisCys131–Lys62 (S131C/S155C)) were examined. In particular, the dynamics and stability of the resulting substituted triads were examined along with their inter-triad residue hydrogen bonding interactions as well as those with other nearby residues and the MLA substrate. The present results suggest that some mutations are more impactful than others. Indeed, mutation of cisSer131 to cisCys131 disrupts the triad and causes inconsistent hydrogen bonding interactions among the triad residues (i.e., Ser155, Cys131, and Lys62). In contrast, in the double mutant Cys155– cisCys131–Lys62, the triad's residues appear to exhibit greater conformation stability with more consistent hydrogen bond interactions, though not necessarily as in the wild type.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".