Rational Engineering of Multiple Active Sites in an Ester Hydrolase
Bibliographic record
Abstract
Effects of altering the properties of an active site in an enzymatic homogeneous catalyst have been extensively reported. However, the possibility of increasing the number of such sites, as commonly done in heterogeneous catalytic materials, remains unexplored, particularly because those have to accommodate appropriate residues in specific configurations. This possibility was investigated by using a serine ester hydrolase as the target enzyme. By using the Protein Energy Landscape Exploration software, which maps ligand diffusion and binding, we found a potential binding pocket capable of holding an extra catalytic triad and oxyanion hole contacts. By introducing two mutations, this binding pocket became a catalytic site. Its substrate specificity, substrate preference, and catalytic activity were different from those of the native site of the wild type ester hydrolase and other hydrolases, due to the differences in the active site architecture. Converting the binding pocket into an extra catalytic active site was proven to be a successful approach to create a serine ester hydrolase with two functional reactive groups. Our results illustrate the accuracy and predictive nature of modern modeling techniques, opening novel catalytic opportunities coming from the presence of different catalytic environments in single enzymes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".