Mining Thermophile Genomes for New PETases with Exceptional Thermostabilities Using Sequence Similarity Networks
Bibliographic record
Abstract
Enzymatic hydrolysis of polyethylene terephthalate (PET) is a promising technology for advancing a circular PET economy. Several PET‐degrading α/β hydrolases have been identified, but the full potential of this enzyme family to catalyze PET hydrolysis remains largely unexplored. To address this, sequence similarity networks are employed to investigate the α/β hydrolase fold‐5 subfamily (IPR029059) for new PETases. Priority is given to sequences from thermophiles, as thermostable enzymes are likely more suitable for industrial applications. Ten enzymes with ≈20% sequence identity to the well‐known LCC‐PETase are identified, and seven are successfully overexpressed and purified for in vitro characterization. Each enzyme catalyzes the hydrolysis of p‐nitrophenyl butyrate, a mimic of trimeric PET, and emulsified PET nanoparticles. Notably, three enzymes are also capable of hydrolyzing PET films. Novel PETases exhibit melting temperatures (Tm) exceeding 55 °C and only modest losses of activity after incubation at 70 °C for 24 h. The crystal structure of AroC (Tm = 85 °C) is resolved to 2.2 Å, revealing several salt bridges that likely confer thermostability, and a unique loop that is conserved among the PETases described here. These novel enzymes will enable engineering campaigns to generate thermostable and catalytically efficient PETases for use as industrial biocatalysts.
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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 | 0.001 |
| 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 | 0.001 |
| 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".