Microbial enzymes for the biodegradation of polylactic acid (PLA): molecular docking simulation and biodegradation pathway proposal
Bibliographic record
Abstract
Polylactic acid (PLA), as a biodegradable polymer, finds widespread utility across diverse sectors, encompassing transportation, agriculture, biomedicine, textiles, electronics, and food packaging. Nonetheless, its degradation kinetics exhibit significant environmental sensitivity, necessitating the development of novel biotechnological methodologies to investigate the microbial and enzymatic constituents implicated in PLA biodegradation. In this review, molecular docking simulations are discussed to analyze the enzymatic degradation of PLA using specific enzymes: proteinase K, PLA depolymerase, and lipase. The binding energy, binding affinity, and dimensions of PLA-binding sites within the enzyme cavities have been examined for each enzyme. The binding amino acid residues mainly include serine, histidine, and aspartate for all plastics, while the interactions involve Van der Waals forces, conventional hydrogen bonding, carbon-hydrogen bonding, and Pi-interactions. Based on our synthesis of the literature, a theoretical PLA biodegradation pathway that illustrates the interaction pattern between PLA and the enzymes, as well as the degradation mechanism is proposed. Our review indicates that in natural environments, enzymes work synergistically, utilizing their unique properties to facilitate an efficient sequential catalytic process for PLA degradation. This integrative approach elucidates the mechanistic underpinnings and metabolic cascades governing PLA degradation, enriching insights into enzymatic catalysis and microbial enzyme-mediated plastic biotransformation. Consequently, a comprehensive understanding of these processes is paramount in optimizing biodegradation kinetics and enhancing the environmental performance of biodegradable polymers. This review provides a detailed framework for refining PLA management strategies and advancing sustainable polymer utilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".