Screening and Characterization of Novel Polyesterases from Environmental Metagenomes with High Hydrolytic Activity against Synthetic Polyesters
Bibliographic record
Abstract
The continuous growth of global plastics production, including polyesters, has resulted in increasing plastic pollution and subsequent negative environmental impacts. Therefore, enzyme-catalyzed depolymerization of synthetic polyesters as a plastics recycling approach has become a focus of research. In this study, we screened over 200 purified uncharacterized hydrolases from environmental metagenomes and sequenced microbial genomes and identified at least 10 proteins with high hydrolytic activity against synthetic polyesters. These include the metagenomic esterases MGS0156 and GEN0105, which hydrolyzed polylactic acid (PLA), polycaprolactone, as well as bis(benzoyloxyethyl)-terephthalate. With solid PLA as a substrate, both enzymes produced a mixture of lactic acid monomers, dimers, and higher oligomers as products. The crystal structure of MGS0156 was determined at 1.95 Å resolution and revealed a modified α/β hydrolase fold, with a lid domain and highly hydrophobic active site. Mutational studies of MGS0156 identified the residues critical for hydrolytic activity against both polyester and monoester substrates, with two-times higher polyesterase activity in the MGS0156 L169A mutant protein. Thus, our work identified novel, highly active polyesterases in environmental metagenomes and provided molecular insights into their activity, thereby augmenting our understanding of enzymatic polyester 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".