Computational Approach Choice in Modeling Flexible Enzyme Active Sites
Bibliographic record
Abstract
The last step in the reductase step of the catalytic mechanism of MsrB was re-investigated using several computational approaches. Our previous QM-cluster paper showed that two possible mechanisms could occur, however the direct formation of disulfide from sulfonium cation was favored over sulfenic acid formation. In contrary, experimental studies suggest sulfenic acid formation. Therefore, first, we investigated the effect of level of theory, which confirmed previous conclusion. In addition, the effect of model choice was also investigated using ONIOM including a large QM layer around Cys440. Interestingly, deprotonating Cys440 leads to direct nucleophilic attack on Cys495 forming disulfide. Second, to eliminate the possibility that all previous results are an artifact of the used crystal structure in which the S...S distance is 3.29 Å, we ran a 5 ns MD simulation on the sulfonium cation intermediate. Surprisingly, our results show that the distance between the two sulfur is significantly increased to 4.88 Å. More importantly a water molecule is located in a proper position for nucleophilic attack. QM/MM calculations shows that sulfenic acid is formed via low barrier of 16.7 kJ mol-1. Finally, the effect of substrate binding on the two Cys's distance were investigated via running several MD simulations of possible intermediates, showing that substrate binding induces conformational changes increasing the sulfur's distance which is decreased upon substrate removal upon sulfenic acid formation. These results question the applicability of QM cluster approach in systems including flexible turns. It also emphasizes the importance of proper preparation of the starting structure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".