Fusion of Hydrophobic Anchor Peptides Promotes the Hydrolytic Activity of PETase but not the Extent of PET Depolymerization
Bibliographic record
Abstract
Abstract Enzymatic recycling of polyethylene terephthalate (PET) has attracted significant attention in recent years. While the fusion of anchor peptides to PET hydrolases is believed to enhance PET hydrolytic activity, a quantitative analysis is yet lacking. Here, we construct four fusion enzymes by fusing anchor peptides (including hydrophobic LCI, LCIM1 and TA2, and hydrophilic EK4) to the C terminus of HotPETase, one of the most active PET hydrolases for high‐crystallinity PET (HC‐PET). Single‐molecule force spectroscopy (SMFS) demonstrates that hydrophobic anchor peptides promote adhesive interactions between the fusion enzymes and the PET surface. This is also validated by the adsorption kinetics and isotherms, and the saturated adsorption capacity remains unaltered compared to HotPETase. At low substrate loadings, the apparent hydrolytic activity of these fusion enzymes is positively related to the hydrophobicity of the anchor peptides. Among them, HotPETase‐LCI stands out as the most effective enzyme for HC‐PET degradation, demonstrating a 1.5‐fold increase in hydrolytic activity. At high substrate loadings, the advantages of fusion with anchor peptides diminish. We conclude that fusion enzymes only facilitate the hydrolytic rates of reactions for HC‐PET but have little effect on the final conversion extent.
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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".