Biochemical and Structural Insights into Enzymatic Depolymerization of Polylactic Acid and Other Polyesters by Microbial Carboxylesterases
Why this work is in the frame
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Bibliographic record
Abstract
Polylactic acid (PLA) is a biodegradable polyester derived from renewable resources, which is a leading candidate for the replacement of traditional petroleum-based polymers. Since the global production of PLA is quickly growing, there is an urgent need for the development of efficient recycling technologies, which will produce lactic acid instead of CO2 as the final product. After screening 90 purified microbial α/β-hydrolases, we identified hydrolytic activity against emulsified PLA in two uncharacterized proteins, ABO2449 from Alcanivorax borkumensis and RPA1511 from Rhodopseudomonas palustris. Both enzymes were also active against emulsified polycaprolactone and other polyesters as well as against soluble α-naphthyl and p-nitrophenyl monoesters. In addition, both ABO2449 and RPA1511 catalyzed complete or extensive hydrolysis of solid PLA with the production of lactic acid monomers, dimers, and larger oligomers as products. The crystal structure of RPA1511 was determined at 2.2 Å resolution and revealed a classical α/β-hydrolase fold with a wide-open active site containing a molecule of polyethylene glycol bound near the catalytic triad Ser114-His270-Asp242. Site-directed mutagenesis of both proteins demonstrated that the catalytic triad residues are important for the hydrolysis of both monoester and polyester substrates. We also identified several residues in RPA1511 (Gln172, Leu212, Met215, Trp218, and Leu220) and ABO2449 (Phe38 and Leu152), which were not essential for activity against soluble monoesters but were found to be critical for the hydrolysis of PLA. Our results indicate that microbial carboxyl esterases can efficiently hydrolyze various polyesters making them attractive biocatalysts for plastics depolymerization and recycling.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it