Polyethylene terephthalate (PET), with excellent mechanical, chemical and thermal properties, is one of the most widely used plastics in the world and is mainly used in the manufacture of packaging materials and beverage bottles. It is estimated that the global production of PET will reach 35.28 million metric tons in 2024, which is a 15% increase compared to 2019 ^[1].

PET can be disposed of by landfill method and other methods of disposal. However, PET plastics are difficult to decompose in the natural environment, and over time, some of them will gradually decompose into microplastics, which are plastic fragments with a diameter of less than 5 mm. PET microplastics will be accidentally eaten by marine organisms and enter the food chain, causing potential hazards to human health including gastrointestinal diseases, respiratory problems, and cancer, which also threats the entire ecosystem (Fig. 1) ^[2].

Fig. 1   The destruction of ecology of PET

At present, there are various ways to degrade microplastics, such as photodegradation, thermal degradation and enzymatic degradation. However, photo-degradation is currently in its infancy and has a low production efficiency; thermal degradation uses higher temperatures and additional reagents, which increases operating costs. And enzymatic degradation decomposes and transforms PET through the action of specific enzymes, which does not leave behind potentially hazardous substances, and is more environmentally friendly, efficient, and sustainable ^[3].

In 2016, a novel PET hydrolase called IsPETase from Ideonella sakaiensis 201-F6 was discovered ^[4]. So far, IsPETase is the true PET-degrading enzyme produced by evolution in nature, so it is considered as a promising application of PET degradation tool.

However, due to the surface hydrophobicity of PET, it is difficult for PETase to directly bind to it, which negatively affects the hydrolysis rate of PETase on PET ^[5]. In the process of reviewing the literature, we learned that plastic-binding peptides, as a class of short polypeptides, can bind to the surface of plastics through hydrophobic binding and other forces, which is a potential solution to increase the degradation ability of PETase to PET. In recent years, scholars from many countries have tried to develop PET-binding peptides to enhance PETase's ability to degrade PET in the environment (Table 1).

Table 1 Representative report of plastic-binding peptides

However, currently, the sources of PET-binding peptides are limited and usually excavated by trial-and-error method, which is inefficient, time-consuming and costly. Therefore, it is an urgent problem to explore efficient methods to discover PET-binding peptides for the efficient degradation of PET microplastics by PETase.

Our lab currently has research on PETase and fusion proteins, such as constructing fusion proteins using carbohydrate binding modules (CBM) with cutinase. We would like to extend such ideas by constructing fusion proteins from PETase with PET-binding peptides.

In recent years, the field of computer algorithms has been booming and has opened up new opportunities. Wuxi, China, which boasts a supercomputing center, has created a good atmosphere for scientific exploration and provided us with inspiration to use deep learning algorithmic strategies in mining PET-binding peptides.

The iGEM previous years wiki is also a good reference for us, for example, the 2023 NNU-CHINA project, which utilizes machine learning, molecular simulation, has something to teach us. Moreover, projects such as TJUSLS-China in 2023 and Vilnius-Lithuania in 2022 have also utilized synthetic biology to address research in plastic pollution, which inspired us.

Our project name is "The PET Degradation STORM: Unleashing PETase's Power with PET-Binding Peptides", which is based on deep learning algorithms and synthetic biology to mine efficient PET-binding peptides and construct fusion proteins with PETase to enhance its ability to bind to PET microplastics for environmental degradation. Based on deep learning algorithms, we will use synthetic biology to explore efficient PET-binding peptides and construct fusion proteins with PETase, which will enhance PETase's power with PET microplastics, and help PETase's application in the environmental degradation of PET microplastics.

Synthetic biology uses the engineering design concept to design, modify and even re-synthesize organisms in a targeted manner, and assembles them into biological systems by rationally designing and synthesizing biological components. Through deep learning screening, we obtained highly efficient PET-binding peptides, and used them as a basic component to design a "short peptide-linker-enzyme" fusion protein using linker elements. Subsequently, we selected the pET-21b vector to construct the expression plasmid of the fusion protein, selected Escherichia coli BL21 (DE3) as the chassis cell to express the fusion proteins, and performed the degradation reaction to validate and characterize them. Based on the "Design-Build-Test-Learn" strategy of synthetic biology, we analyzed the problems through the results of the experiments at each stage, and optimized the design of the fusion protein, the fermentation conditions, the selection of linkers to improve the degradation efficiency of PETase with the help of PET- binding peptide to the greatest extent. (See Engineering )

In total, we propose a solution strategy, abbreviated as STORM, which means Screen, Test, Optimization, and Re-Modification, and the process is illustrated in Fig. 2.(See Design )

Fig. 2   Overall research strategy

Screen: Establishment of one-dimensional LSTM model for initial screening and three-dimensional GCN model for re-screening to screen efficient PET-binding peptides.

Test: Construction of fusion proteins by fusing short peptides with the N-terminus and C-terminus of PETase, respectively, for validation and characterization.

Optimization: Optimization of fermentation conditions as well as linker to improve the expression of fusion proteins.

Re-Modification: Introduction of beneficial mutations to PET-binding peptides to improve the substrate binding efficiency of fusion proteins.

Our objective is to elevate the PET-degrading prowess of PETase by fusing it with PET-binding peptides, thereby advancing the practical application of PET biodegradation. Leveraging deep learning models, we have screened and identified three PET-binding peptides with robust PET-binding properties, 50-4-N, 50-5-C, and 60-2-N. Notably, the fusion proteins created with these peptides exhibited degradation efficiencies of 144.67%, 126.22%, and 146.97% compared to PETase alone. Furthermore, we conducted molecular modifications on these peptides and successfully derived beneficial mutants of fusion proteins: 50-4-N-V19I-G4S-PETase, PETase-G4S-50-5-C-R287V, and 60-2-N-L60I-SLE-PETase. These mutants displayed enhanced properties, with their corresponding fusion proteins achieving degradation efficiencies of 193.08%, 207.49%, and 187.32% respectively, in comparison to PETase. These findings validate that our research project has met its anticipated goals, offering valuable insights and substantial support for the biodegradation of PET.

In the upcoming phase, we aim to delve into the potential of combining multipoint beneficial mutants of PET-binding peptides, based on our current findings, to further enhance their binding capabilities. Meanwhile, these PET-binding peptides hold promise in augmenting the degradation efficiency of other PET-degrading enzymes. We harbor aspirations to assist other iGEM teams, while also serving as a source of inspiration for devising more effective strategies to tackle the challenge of PET microplastic degradation in the future.

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