Nature versus nurture in two highly enantioselective esterases from <i>Bacillus cereus</i> and <i>Thermoanaerobacter tengcongensis</i>
Bibliographic record
Abstract
There is an increasing need for the use of biocatalysis to obtain enantiopure compounds as chiral building blocks for drug synthesis such as antibiotics. The principal findings of this study are: (i) the complete sequenced genomes of Bacillus cereus ATCC 14579 and Thermoanaerobacter tengcongensis MB4 contain a hitherto undescribed enantioselective and alkaliphilic esterase (BcEST and TtEST respectively) that is specific for the production of (R)-2-benzyloxy-propionic acid ethyl ester, a key intermediate in the synthesis of levofloxacin, a potent antibiotic; and (ii) directed evolution targeted for increased thermostability of BcEST produced two improved variants, but in either case the 3-5 °C increase in the apparent melting temperature (T(m)) of the mutants over the native BcEST that has a T(m) of 50 °C was outperformed by TtEST, a naturally occurring homologue with a T(m) of 65 °C. Protein modelling of BcEST mapped the S148C and K272R mutations at protein surface and the I88T and Q110L mutations at more buried locations. This work expands the repertoire of characterized members of the α/β-fold hydrolase superfamily. Further, it shows that genome mining is an economical option for new biocatalyst discovery and we provide a rare example of a naturally occurring thermostable biocatalyst that outperforms experimentally evolved homologues that carry out the same hydrolysis.
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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) | 0.000 | 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) | 0.000 | 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".