Advances in microbial exoenzymes bioengineering for improvement of bioplastics degradation
Bibliographic record
Abstract
Plastic pollution has become a major global concern, posing numerous challenges for the environment and wildlife. Most conventional ways of plastics degradation are inefficient and cause great damage to ecosystems. The development of biodegradable plastics offers a promising solution for waste management. These plastics are designed to break down under various conditions, opening up new possibilities to mitigate the negative impact of traditional plastics. Microbes, including bacteria and fungi, play a crucial role in the degradation of bioplastics by producing and secreting extracellular enzymes, such as cutinase, lipases, and proteases. However, these microbial enzymes are sensitive to extreme environmental conditions, such as temperature and acidity, affecting their functions and stability. To address these challenges, scientists have employed protein engineering and immobilization techniques to enhance enzyme stability and predict protein structures. Strategies such as improving enzyme and substrate interaction, increasing enzyme thermostability, reinforcing the bonding between the active site of the enzyme and substrate, and refining enzyme activity are being utilized to boost enzyme immobilization and functionality. Recently, bioengineering through gene cloning and expression in potential microorganisms, has revolutionized the biodegradation of bioplastics. This review aimed to discuss the most recent protein engineering strategies for modifying bioplastic-degrading enzymes in terms of stability and functionality, including enzyme thermostability enhancement, reinforcing the substrate binding to the enzyme active site, refining with other enzymes, and improvement of enzyme surface and substrate action. Additionally, discovered bioplastic-degrading exoenzymes by metagenomics techniques were emphasized.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".