Relationships between Substrate Promiscuity and Chiral Selectivity of Esterases from Phylogenetically and Environmentally Diverse Microorganisms
Bibliographic record
Abstract
Substrate specificity and selectivity of a biocatalyst are determined by the protein sequence and structure of its active site. Finding versatile biocatalysts acting against multiple substrates while at the same time being chiral selective is of interest for the pharmaceutical and chemical industry. However, the relationships between these two properties in natural microbial enzymes remain underexplored. Here, we performed an experimental analysis of substrate promiscuity and chiral selectivity in a set of 145 purified esterases from phylogenetically and environmentally diverse microorganisms, which were assayed against 96 diverse esters, 20 of which were enantiomers. Our results revealed a negative correlation between substrate promiscuity and chiral selectivity in the evaluated enzymes. Esterases displaying prominent substrate promiscuity and large catalytic environments are characterized by low chiral selectivity, a feature that has limited commercial value. Although a low level of substrate promiscuity does not guarantee high chiral selectivity, the probability that esterases with smaller active sites possess chiral selectivity factors of interest for industry (>25) is significantly higher than for promiscuous enzymes. Together, the present study unambiguously demonstrates that promiscuous and selective esterases appear to be rare in nature and that substrate promiscuity can be used as an indicator of the chiral selectivity level of esterases, and vice versa.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".