Mutations in Distant Residues Moderately Increase the Enantioselectivity of <i>Pseudomonas fluorescens</i> Esterase towards Methyl 3Bromo‐2‐methylpropanoate and Ethyl 3Phenylbutyrate
Bibliographic record
Abstract
Directed evolution combined with saturation mutagenesis identified six different point mutations that each moderately increases the enantioselectivity of an esterase from Pseudomonas fluorescens (PFE) towards either of two chiral synthons. Directed evolution identified a Thr230Ile mutation that increased the enantioselectivity from 12 to 19 towards methyl (S)-3-bromo-2-methylpropanoate. Saturation mutagenesis at Thr230 identified another mutant, Thr230Pro, with higher-than-wild-type enantioselectivity (E=17). Previous directed evolution identified mutants Asp158Asn and Leu181Gln that increased the enantioselectivity from 3.5 to 5.8 and 6.6, respectively, towards ethyl (R)-3-phenylbutyrate. In this work, saturation mutagenesis identified other mutations that further increase the enantioselectivity to 12 (Asp158Leu) and 10 (Leu181Ser). A homology model of PFE indicates that all mutations lie outside the active site, 12-14 A from the substrate and suggests how the distant mutations might indirectly change the substrate-binding site. Since proteins contain many more residues far from the active site than close to the active site, random mutagenesis is strongly biased in favor of distant mutations. Directed evolution rarely screens all mutations, so it usually finds the distant mutations because they are more common, but probably not the most effective.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".