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 ( T m ) exceeding 55 °C and only modest losses of activity after incubation at 70 °C for 24 h. The crystal structure of AroC ( T m = 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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".