Enhanced Adsorption and Enzymatic Hydrolysis of Polyethylene Terephthalate by Cutinase with an N-Terminal Hydrophobic Tether
Bibliographic record
Abstract
The environmental challenges presented by plastic waste, particularly polyethylene terephthalate (PET), necessitates innovative biodegradation strategies. The cutinase from Thermobifida cellulosilytica, Thc_Cut1 (Cut), was site-specifically conjugated with alkyl tethers of varying lengths (C3, C6, C9) through 1H-1,2,3-triazole-4-carbaldehyde (TA4C) derivatives. These conjugations were provided to enhance affinity for PET by adjusting the enzyme's hydrophobicity. The enzyme activity and kinetic parameters of both conjugated and unconjugated cutinases revealed that the modifications have minimal impact on catalytic activity. However, a significant improvement in PET hydrolysis efficiency was observed. Specifically, hexyl and nonyl TA4C-containing cutinase display notable increases in terephthalic acid (TPA) release, exceeding the performance of unconjugated cutinase by 65% and 69%, respectively. Scanning electron microscopy and water contact angle measurements confirmed the enhanced erosion and hydrophilicity of the PET surface following enzyme treatment. Increased enzyme adsorption on the PET surface for C6-Cut and C9-Cut was validated by X-ray photoelectron spectroscopy. Moreover, high-speed atomic force microscopy demonstrated faster and more stable adsorption of C6-Cut and C9-Cut on PET surfaces compared to the slower adsorption of unconjugated cutinase. Additionally, molecular dynamics simulations indicate a higher affinity of conjugated cutinase for PET film. These results suggest that conjugating an alkyl tether to the N-terminus strengthens the interaction between cutinase and PET, improving 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 | 0.000 |
| 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 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".