A general and efficient strategy for generating the stable enzymes
Bibliographic record
Abstract
Abstract The local flexibility of an enzyme’s active center plays pivotal roles in catalysis, however, little is known about how the flexibility of these flexible residues affects stability. In this study, we proposed an active center stabilization (ACS) strategy to improve the kinetic thermostability of Candida rugosa lipase1. Based on the B-factor ranking at the region ~10 Å within the catalytic Ser209, 18 residues were selected for site-saturation mutagenesis. Based on three-tier high-throughput screening and ordered recombination mutagenesis, the mutant VarB3 (F344I/F434Y/F133Y/F121Y) was shown to be the most stable, with a 40-fold longer in half-life at 60 °C and a 12.7 °C higher Tm value than that of the wild type, without a decrease in catalytic activity. Further analysis of enzymes with different structural complexities revealed that focusing mutations on the flexible residues within around 10 Å of the catalytic residue might increase the success rate for enzyme stabilization. In summary, this study identifies a panel of flexible residues within the active center that affect enzyme stability. This finding not only provides clues regarding the molecular evolution of enzyme stability but also indicates that ACS is a general and efficient strategy for exploring the functional robustness of enzymes for industrial applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".