A full picture of enzymatic catalysis by hydroxynitrile lyases from <i>Hevea brasiliensis</i> : protonation dependent reaction steps and residue-gated movement of the substrate and the product
Bibliographic record
Abstract
Hydroxynitrile lyases (HNLs) defend plants from herbivores and microbial attack by releasing cyanide from hydroxynitriles. The reverse process has been productively applied to bioorganic syntheses of pharmaceuticals and agrochemicals. To improve our understanding of the catalytic mechanism of HNLs, extensive ab initio QM/MM and classical MM molecular dynamics simulations have been performed to explore the catalytic conversion of cyanohydrins into aldehyde (or ketone) and HCN by hydroxynitrile lyases from Hevea brasiliensis (HbHNLs). It was found that the catalytic reaction approximately follows a two-stage mechanism. The first stage involves two fast processes including the proton abstraction of the substrate through a double-proton transfer and the C-CN bond cleavage, while the second stage concerns HCN formation and is rate-determining. The complete free energy profile exhibits a peak of ∼18 kcal mol(-1). Interestingly, the protonation state of Lys236 influences the efficiency of the enzyme only to some extent, but it changes the entire catalytic mechanism. The dynamical behaviors of substrate delivery and HCN release are basically modulated by the gate movement of Trp128. The remarkable exothermicity of substrate binding and the facile release of HCN may drive the enzyme-catalyzed reaction to proceed along the substrate decomposition efficiently. Computational mutagenesis reveals the key residues which play an important role in the catalytic process.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".