Mining bacterial genomes for novel arylesterase activity
Bibliographic record
Abstract
One hundred and seventy-one genes encoding potential esterases from 11 bacterial genomes were cloned and overexpressed in Escherichia coli; 74 of the clones produced soluble proteins. All 74 soluble proteins were purified and screened for esterase activity; 36 proteins showed carboxyl esterase activity on short-chain esters, 17 demonstrated arylesterase activity, while 38 proteins did not exhibit any activity towards the test substrates. Esterases from Rhodopseudomonas palustris (RpEST-1, RpEST-2 and RpEST-3), Pseudomonas putida (PpEST-1, PpEST-2 and PpEST-3), Pseudomonas aeruginosa (PaEST-1) and Streptomyces avermitilis (SavEST-1) were selected for detailed biochemical characterization. All of the enzymes showed optimal activity at neutral or alkaline pH, and the half-life of each enzyme at 50°C ranged from < 5 min to over 5 h. PpEST-3, RpEST-1 and RpEST-2 demonstrated the highest specific activity with pNP-esters; these enzymes were also among the most stable at 50°C and in the presence of detergents, polar and non-polar organic solvents, and imidazolium ionic liquids. Accordingly, these enzymes are particularly interesting targets for subsequent application trials. Finally, biochemical and bioinformatic analyses were compared to reveal sequence features that could be correlated to enzymes with arylesterase activity, facilitating subsequent searches for new esterases in microbial genome sequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".