Polyethylene terephthalate (PET) is a kind of synthetic polyester widely used in packaging and textiles. It is composed of terephthalic acid (TPA) and ethylene glycol (EG) through ester bond. Its low cost, portability, durability and gas barrier ability are widely used in beverage bottles, product packaging and textile industries.

Despite its many advantages, the production and disposal of PET have significant environmental impacts.

• Resource Consumption: In the production process of PET, the consumption of oil resources is significant, and the overuse of oil as a non-renewable natural resource undoubtedly brings great challenges to the environmental sustainability. Therefore, we urgently need to seek out sustainable and environmentally friendly plastic degradation and reuse methods to reduce our dependence on oil resources.
• Carbon emissions: In the production process of PET, high temperature reaction is an indispensable link. However, this process is accompanied by huge energy consumption, which leads to significant carbon emissions, which undoubtedly aggravates the impact of the greenhouse effect.Carbon Emissions: The production process involves high-temperature reactions and consumes a large amount of energy, leading to significant carbon emissions and exacerbating the greenhouse effect.

• Slow Degradation: In the natural environment, the degradation process of PET is extremely long, often taking hundreds of years. This slow degradation rate leads to the gradual accumulation of large amounts of PET waste in the environment, forming the so-called "white pollution", which poses a serious challenge to the natural environment.
• Ecological Pollution: Ecosystems are also one of the main victims of PET plastic pollution, especially in Marine ecosystems. Plastic waste is often accidentally consumed by Marine life, leading to their death from indigestion. In addition, plastic waste hangs around the bodies of Marine life, limiting their movements and even causing them to suffocate.
• Harm to human health: In addition, microplastics (plastic particles less than 5 mm) accumulate through the food chain and may ultimately affect human health.
  Microplastics are small, not easy to be filtered, whether it is the urban sewage treatment plant, or the human body itself, it is difficult to play a protective role. As the top of the food chain, over time, microplastics will be enriched in the body over time. Although most of these microplastics will be excreted in the feces, some will still be left in the body, causing unpredictable harm.

Although PET has high recycling value, the global recycling rate remains low. Many PET wastes fail to enter the recycling system, ending up in landfills or being incinerated, resulting in resource wastage and environmental pollution. The recycling process, involving cleaning, sorting, and reprocessing, requires significant energy and water resources, and recycled PET often does not match the quality of virgin material. Traditional chemical degradation methods, such as high-temperature pyrolysis or solvent treatment, are typically energy-intensive and produce harmful by-products, which do not align with sustainable development goals.

To address the environmental issues of PET, scientists have started researching biodegradation methods, especially the use of enzymes to degrade PET.

1) Discovery of Enzymes: Since the discovery of a PET hydrolase from Thermobifida fusca in 2005. In 2016, Japanese researchers isolated an enzyme called PETase from a bacterium named Ideonella sakaiensis. This enzyme can break down PET.

2) Improvement of Enzymes: Subsequent studies have shown that genetic engineering can enhance the activity of PETase, accelerating the degradation of PET. Our laboratory has made significant progress in the design and directed evolution of PETase.

3) Industrial Application: Currently, some companies are exploring the use of PET-degrading enzymes in industrial-scale PET recycling processes to improve recycling efficiency and quality.

Although the PET degrading enzyme has shown excellent performance in the laboratory environment, we still need to face many challenges in the process of promoting its industrial application. The core issues are concentrated in the following aspects:

1) High Production Costs: Firstly, the high production cost is a big obstacle to the wide application of the PET degrading enzyme. The current production process leads to the high cost of the enzyme, and it is difficult to realize large-scale commercial application. According to the existing literature, even if the genetically engineered PETase is expressed in Escherichia coli, its yield can only reach the level of hundreds of milligrams per liter of culture medium, which is far from the requirements of industrial large-scale application.

2) Low Production Efficiency: Secondly, low production efficiency is another problem to be solved urgently. The existing enzyme expression system is inefficient, which limits the possibility of large-scale production. For example, the expression of MHETase in natural reservoir is low, which affects the efficiency of the whole degradation process.

In order to deal with the above problems, researchers adopted diversified strategies to improve the yield and activity of enzymes. At present, the main strategies to improve enzyme production include:

• introducing chassis cells with high expression ability;
• Co-expressing auxiliary factors such as solubilizing labels and molecular chaperones;
• Optimizing the promoter sequence to enhance the gene expression efficiency;
li>improve the design of signal peptide to promote the exocrine process of enzyme;
• Fine regulation of gene dosage to achieve the best expression balance;
• Implement high-density fermentation process to improve the overall production efficiency.

However, due to factors such as the difficulty of transforming chassis cells and the limitations of expression regulation technology, these transformation strategies have not yet reached the standard of industrial production. Therefore, continuous efforts are needed to further optimize these strategies in order to realize efficient industrial production of enzymes!

• 1) E. coli Strain and Vector Screening
  In the "design-build-test-learn" cycle, we not only screened E. coli strains but also various vectors to identify the best combination for ICCG expression. This ensured a stable and efficient expression platform, laying a solid foundation for subsequent optimization.
• 2) Optimization of Culture Conditions
  To further enhance ICCG expression efficiency, we conducted two rounds of "design-build-test-learn" cycles, focusing on optimizing induction time, induction temperature, and IPTG concentration. Through these adjustments, we identified the optimal culture conditions, significantly improving ICCG expression levels and ensuring reproducibility and efficiency in experimental results.
• 3) Promoter Optimization
  Promoter optimization underwent three rounds of "design-build-test-learn" cycles. We first generated the Mtac promoter library through random mutation and used experimental screening data to train the promoter prediction model. Based on the model, we designed and generated the DMtac promoter, and through experimental validation of its expression efficiency, we ultimately obtained an efficient promoter sequence that provided a solid foundation for high ICCG expression.
• 4) Signal Peptide Screening and Optimization
  Signal peptide optimization followed three rounds of "design-build-test-learn" cycles. In the first round, we performed model prediction on 200 signal peptides from the E. coli signal peptide library and selected 40 signal peptides for wet lab experiments. Eventually, we chose the nfaA signal peptide for further optimization. In the subsequent two rounds of optimization, we generated the MnfaA mutant through mutations and experimentally validated its enhancement in ICCG secretion, ultimately obtaining an optimized signal peptide sequence.
• 5) Protein Expression Characterization
  We constructed the ICCG-GFP fusion protein, using GFP fluorescence intensity to characterize ICCG expression levels. Additionally, we built a high-throughput screening system to quickly screen and evaluate changes in protein expression under different conditions. This high-throughput system provided powerful tools for our optimization, accelerating the experimental process.

Our model is constructed based on two aspects: promoters and signal peptides.

1) Promoters: Our model is a generator-predictor framework. Based on the randomly mutated promoter dataset provided by the experimental group, we used a CNN-LSTM model (Convolutional Neural Network and Long Short-Term Memory Network) to predict promoter expression strength based on sequence data and created a promoter strength predictor. Then, we applied a genetic algorithm to generate mutated promoters and used the previously developed predictor as an evaluation tool to optimize the promoters. Later, we improved the generator by switching from the genetic algorithm to a more efficient and interpretable binomial statistical model.

2) Signal Peptides:Similarly, our signal peptide model follows a generator-predictor structure. Using a large dataset of signal peptides obtained from a library, we trained a predictor using a random forest model and screened a small subset of signal peptides to provide to the wet lab group. After receiving the signal peptide data returned by the wet lab, we fine-tuned the predictor with this small dataset. In addition, we employed a Hidden Markov Model (HMM) as the generator to produce mutants of the best-performing signal peptides from the previous wet lab experiments, which were then evaluated using the predictor.

We successfully constructed a highly efficient expression strain, with expression efficiency improved by x% compared to the original system. The efficient ICCG expression system we developed increased ICCG yield by xx% compared to the initial conditions reported in the literature.

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