iGEM 2023 — Team nnu-china

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## **May**

### **Wet Lab**

1. Read many papers to understand the significance of ancestral sequences and machine learning.
2. We visited the laboratory to learn basic laboratory operations and how to properly dispose of laboratory waste.

### **Human Practice**

1. We held our first IGEM presentation, through which we had a clearer plan and identified common goals to work towards.

2. In order to better understand the biological explanation of PET plastics, we went to the School of Environment of Nanjing Normal University and interviewed Professor Fengxiao Zhu.

3. In order to improve mutual understanding and communication among team members, we held our first team building activity.

4. We participated in the IGEM Alliance Nanjing Regional Meeting organised by Nanjing University and the on-campus publicity.

## **June**

### **Wet Lab**

1. Learn how to reconstruct ancestral sequences and use machine learning algorithms to predict the potential variants.

2. We extracted the ASR1-PETase plasmid, and ASR1-PETase variants were constructed using a two-step PCR method for DNA amplification. The recombinant plasmids above were transformed into *Escherichia coli* BL21 (DE3) for protein expression.

3. Learn to perform MD simulations using GROMACS 2022 simulation package with the GROMOS96 (54a7) force field.

### **Human Practice**

1. In order to further promote the green concept of iGEM team and get more practical inspiration, our team participated in Biological Carnival.

2. We went to the Nanjing Quality Supervision Institute to visit, study and interview.

3. We went to Nanjing Refinery Primary School, Nanjing Huashu Village community, and Jurong City Children's Palace to introduce our team, synthetic biology and basic knowledge about plastics to the children in a simple and easily comprehensible manner. For young and middle-aged groups, we carried out science popularization activities in Xianlin Lake community. For the elderly, we went to Suojin Four Village community to introduce our project and plastic-related knowledge to the elderly.

## **July**

### **Wet Lab**

1. We constructed a PETase phylogenetic tree and reconstructed ancestral sequences for evolutionary nodes. Experimentally, the ancestral sequences of PETase constructed into pET-22b(+) vector by Gibson assembly technique and verified the activity by protein expression.

2. For the promising ASR1-PETase, we used the ESM-1v model to design variants and compare it with other machine learning algorithms .

3. We verified the hydrolytic activity of ASR1-PETase variants using BHET as the substrate and processed experimental data using Graphpad Prism software.

4. Learn to perform MD simulations using GROMACS 2022 simulation package with the GROMOS96 (54a7) force field.

### **Human Practice**

1. Our team embarked on a campus initiative to collect PET plastic bottles, subsequently employing the creative ingenuity of our members to metamorphose these bottles into an array of exquisite artworks.

2. We carried out a debate on the topic of “Whether bioenzyme degradation of plastics is a feasible way to solve plastic pollution”.

3. We organized the environmental protection and sustainable development summit and invited eight teams.

4. In order to further explore more efficient ways of environmental protection and deeper exploration in the field of synthetic biology, our team came to Nanjing Vazyme Bioiech Co., Lid. for a visit and study.

5. We visited Zhejiang Kingsun Eco-Pack Co., Ltd.

6. To get a detailed understanding of the waste disposal process, we came to Suzhou Everbright Environmental Protection Energy Co., Ltd.

7. We visited Zhihe Biotechnology Co.,Ltd.

## **August**

### **Wet Lab**

1. We modified the phylogenetic tree and processed experimental data using Graphpad Prism software.

2. Plotting machine learning schematics and visualization of substitution using PYMOL.

3. We verified the hydrolytic activity of ASR1-PETase variants upon PET film, and also constructed a two-enzyme system to degrade PET into the homogeneous TPA. Moreover, we regenerate the virgin PET using TPA as a substrate to achieve the closed-loop recycling of PET.

4. MD was performed on the wild type PETase and variants at different temperatures to figure out the driving force behind the increased degradation capacity at high temperature.

### **Human Practice**

1. We participated in the Bioremediation themed academic forum organized by ZJUT-CHINA. At the meeting, we presented our work and focused on experimental design and human practice collaboration.

2. We participated in the 14th National Symposium on Enzyme Engineering, learned about the most cutting-edge achievements in the field of enzyme engineering.

## **September**

### **Wet Lab**

1. Collating experimental data and accomplish all the script.

2. Analyzing the MD results and processed the data using Graphpad Prism software.

### **Human Practice**

After the experiments, our team, together with Zhejiang University of Technology, released a questionnaire on plastic degradation, and we found that there is still a need to educate the public about PET biodegradation.

## **October**

### **Human Practice**

To promote synthetic biology widely, our team participated in the biological podcast project and chose the theme of "Science Popularization of Biological knowledge".

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Contact us

Address

 Xuelin Road 2,

Nanjing, Jiangsu, China

Phone

 86-25-85898183

Email Address

  lixiujuan@njnu.edu.cn

© 2023 - Content on this site is licensed under a [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/).

The repository used to create this website is available at [gitlab.igem.org/2023/nnu-china](https://gitlab.igem.org/2023/nnu-china).

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* [Cycle1: Construct the Ancestral Sequence of PETase](#Cycle1:Construct the Ancestral Sequence of PETase)
* [Cycle2: Substitution Predicted by Machine Learning](#Cycle2:Substitution Predicted by Machine Learning)
* [Cycle3: Validation of Variants Performance Through Site-Directed Mutagenesis](#Cycle3:Validation of Variants Performance Through Site-Directed Mutagenesis)
* [Cycle4: Molecular Docking and Molecular Dynamics Simulations](#Cycle4: Molecular Docking and Molecular Dynamics Simulation)
* [Cycle5: Constructing a Two-Enzyme Degradation System to Generate Homogeneous TPA](#Cycle5: Constructing a Two-Enzyme Degradation System to Generate Homogeneous TPA )
* [Cycle6: PET Repolymerization for the Closed-Loop PET Recycling](#Cycle6: PET Repolymerization for the Closed-Loop PET Recycling)

## Cycle1: Construct the Ancestral Sequence of PETase

### Design

In 2016, Yoshida et al. from Kyoto Institute of Technology identified a *Is*PETase isolated from *Ideonella sakaiensis* 201-F6. This enzyme exhibited superior PET hydrolysis activity when degrading commercial bottle-derived PET and PET film under ambient conditions. This discovery shed light on the potential to mitigate the environmental impact of uncollectable PET release. However, the efficiency of *Is*PETase was usually limited by temperature conditions. After interviewing Professor Fengxiao Zhu，we believed the potential of ancestral sequence reconstruction (ASR) to generate PETase with enhanced stability and activity.

### Build

This study opted for the profile-HMM algorithm instead of the BLAST local alignment algorithm when analyzing protein sequences in *silico*. This choice was made to overcome the challenge of low overall sequence similarity between these sequences. In our quest to gather PETase-associated protein sequences from a wide range of species, we utilized the Pfam entry (PF01738) and the HMMER Web server to conduct searches across Swiss-Prot and NCBI public databases. By configuring the expected values (E-value) and including additional sequences for homology identification, we curated a comprehensive database of related protein sequences. Subsequently, we aligned these sequences and constructed a corresponding phylogenetic tree using the maximum likelihood algorithm through the Fireprot server. Ultimately, this analysis yielded seven distinct evolutionary nodes based on the phylogenetic tree **(Fig. 1)**.

![](https://static.igem.wiki/teams/4790/wiki/jinghuashu1012.png)

**Fig. 1** A phylogenetic tree constructed on the basis of Swiss-prot and NCBI databases employing FireProt (https://loschmidt.chemi.muni.cz/fireprotweb/). Number 1-7 represents ASR1 (node 59), ASR2 (node 98), ASR3 (node 100), ASR4 (node 109), ASR5 (node 111), ASR6 (node 112), and ASR7 (node 115).

### Test

We submitted the obtained ancestral sequences to Tsingke Biotechnology Co., Ltd. (Nanjing, China) for synthesis. Subsequently, these synthesized sequences were assembled into the pET-22b(+) vector using the Gibson method **(Fig. 2)** . Following this, we conducted an alignment and activity validation of these ancestral sequences.

![](https://static.igem.wiki/teams/4790/wiki/engineering2.png)

**Fig. 2** Template plasmid of pET-22b/ASR1-PETase profiles.

### Learn

The phylogenetic tree shows that PETase belongs to the superprotein family of dienolide hydrolase **(Fig. 1)**. Enzymes belonging to this protein family historically have the ability to hydrolyze lignin, cutin and other polymers. With plastic's ascendancy as a novel synthetic material, enzymes like cutinase and ligninase have evolved into effective plastic-degrading agents, including PETase.

![](https://static.igem.wiki/teams/4790/wiki/engineering3.png)

**Fig. 3** Activity verification and sequence similarity comparison of ancestral sequences. (a) PET depolymerization by ASR1-7. Reaction conditions: The PET films (ø=6 mm) were soaked in 2000 μL of Na2HPO4-NaH2PO4 (pH 8.0, 50 mM) buffer at 40 °C with 100 μL of 0.5 mg/mL enzyme solution for five days. Error bars correspond to the standard deviation (s.d.) of three measurements (n=3). (b) Sequence identities between wild type PETase and ASR1-7.

The validation of ancestral sequence activity showed significant degradation activity of ASR1-PETase and ASR7-PETase **(Fig. 3a)**. Sequence similarity analysis revealed 67.25% similarity between PETase and ASR7-PETase, while ASR1-PETase exhibited 21.37% similarity to PETase **(Fig. 3b)**.

Sequence and structural alignment between ASR1-7 and PETase highlighted shared attributes: typical α/β-hydrolase folds, conserved catalytic triads, and disulfide bonds **(Fig. 4)**. However, in an evolutionary context, ASR7-PETase bears a shorter distance from PETase, presenting higher sequence similarity and fewer substitutions. In contrast, ASR1's divergence from PETase is more pronounced due to a longer evolutionary span **(Fig. 3b)**, providing more substitutions. Thus, ASR1-PETase emerges as a prime candidate for site-directed mutagenesis, fostering richer variants, of which we will perform site prediction and site-directed mutagenesis.

![](https://static.igem.wiki/teams/4790/wiki/engineering4.png)

**Fig. 4** 3D structures of PETase and ASR1-PETase. Red areas show the catalytic triad (S131-D177-H208 of PETase and C161-D210-H242 of ASR1-PETase).

## Cycle2: Substitution Predicted by Machine Learning

### Design

To enhance the thermostability and catalytic efficiency of ASR1, we are actively utilizing machine learning techniques for mutation prediction. In pursuit of this goal, we have embraced the ESM-1v model with Zero-Shot learning. This algorithm excels in learning from scenarios with limited experimental data or relevant information, enabling it to predict protein positions within a protein effectively. ESM-1v leverages sequence-balanced protein structures based on existing protein knowledge to identify crucial mutation sites and assess their impact on protein functions efficiently. This feature sets it apart from other mutation design models, as illustrated in **Fig. 5**.

![](https://static.igem.wiki/teams/4790/wiki/engineeringfive.png)

**Fig. 5** Comparison of algorithms for ESM-1v, Evmutation, and Deepsequence.

### Build

Following the computations using ESM-1v, 20 potential variants with the highest scores were selected for experimental verification **(Fig. 6)**.

![](https://static.igem.wiki/teams/4790/wiki/engine61081.png)

**Fig. 6** The Mechanism of ESM-1v Algorithm.

### Test

Finally, 20 potential variants with the highest scores (P177E, K249E, I223E, W120R, Q79N, S242G, L246H, Y46I, N81H, G201A, F213S, F275L, L122K, C287P, M17A, V265T, C26A, E125R, M40E, Y6A) were performed by site-directed mutagenesis.

### Learn

**Fig. 7** spatially illustrates the chosen sites within the protein structure. We assumed that these potential sites could significantly impact various protein attributes, such as thermostability, and activity. As a result, site-directed mutagenesis were performed for subsequent experimental validation.

![](https://static.igem.wiki/teams/4790/wiki/engineeringggeri.png)

**Fig. 7** Substitution of ASR1 predicted by machine learning. The red sticks represent the catalytic triad, and the blue sticks represent the substitutions.

## Cycle3: Validation of Variants Performance through Site-Directed Mutagenesis

### Design

To assess the performance of 20 variants, we conducted catalytic reactions using both Bis (2-Hydroxyethyl) terephthalate (BHET, a structurally similar intermediate of PET degradation) and PET substrates across varying temperature conditions.

### Build

We used ASR1 plasmid as template to designed primers **(Table 1)**, which aligned with *E. coli* codon preference and the central genetic dogma. After confirming the PCR product by DNA electrophoresis **(Fig. 8)**, the recombinant plasmid was transformed into *E. coli* DH5α, followed by expressing in *E. coli* BL21 (DE3).

**Table 1** Partial site-directed mutagenesis primers. (Detailed primer designs are in the Parts Page: BBa\_K4790003——BBa\_K4790024 and BBa\_K4790036——BBa\_K4790053.)

| Primer | Sequence (5'-3') |
| --- | --- |
| ASR1-P177E-F | CCAGCTGGCAGCGAAAAATCCGGAAGTTAAAGCTACTGTGTC |
| ASR1-P177E-R | GACACAGTAGCTTTAACTTCCGGATTTTTCGCTGCCAGCTGG |
| ASR1-K249E-F | GCCACGCATTTCTGAACGACGAACGTCCAAGCTACAACAAAG |
| ASR1-K249E-R | CTTTGTTGTAGCTTGGACGTTCGTCGTTCAGAAATGCGTGGC |
| ... | ... |
| ASR1-M40E-F | GCAGACTGACGGTATCGAAGGTGAAGAAATCTCTTATCCGACC |
| ASR1-M40E-R | GGTCGGATAAGAGATTTCTTCACCTTCGATACCGTCAGTCTGC |
| ASR1-Y6A-F | CGACATGGTGGCGAATCCGGCAGAACGTGGTCCAGAT |
| ASR1-Y6A-R | ATCTGGACCACGTTCTGCCGGATTCGCCACCATGTCG |

![](https://static.igem.wiki/teams/4790/wiki/engine8108.png)

**Fig. 8** Agarose gel eletrophoresis analysis of whole plasmid PCR results.

### Test

Using BHET as the substrate, we validate the catalytic activity of variants at 30 °C and 50 °C, respectively. Potential variants were selected to conduct PET film degradation experiments at 30, 40, 50, and 60 °C, respectively.

### Learn

Guided by BHET conversion rates and Terephthalic acid (TPA) content **(Fig. 9)**, ASR1-V265T, ASR1-N81H, ASR1-W120R, and ASR1-L122K were earmarked for PET film validation.

![](https://static.igem.wiki/teams/4790/wiki/engine91012.png)

**Fig. 9** Experimental validation of BHET in 20 variants. (a) Conversion of BHET. (b) Concentration of TPA. Reaction conditions: The BHET (5 mM) was dissolved in 2000 μL Na2HPO4-NaH2PO4 buffer (pH 8.0, 50 mM) at 30 °C and 50 °C with 100 μL of 0.5 mg/mL  enzyme solution for 6 h. Error bars correspond to the standard deviation (s.d.) of three measurements (n = 3).

Through the analysis of PET film validation, accounting for total released PET products **(Fig. 10)** using the formulated scoring strategy (Score = C30 °C\*10% + C40 °C\*20% + C50 °C\*30% + C60 °C\*40%), ASR1-V265T and ASR1-W120R were finalized for Molecular Dynamics (MD) simulations.

![](https://static.igem.wiki/teams/4790/wiki/qqqqqqq.png)

**Fig. 10** Product released from PET depolymerization by ASR1-V265T, ASR1-N81H, ASR1-W120R and ASR1-L122K. Score = C30 °C\*10% + C40 °C\*20% + C50 °C\*30% + C60 °C\*40%. Reaction conditions: The PET films (ø = 6 mm) were soaked in 2000 μL of Na2HPO4-NaH2PO4 buffer (pH 8.0, 50 mM) at 30 °C, 40 °C, 50 °C and 60 °C with 100 μL of 0.5 mg/mL enzyme solution for five days. Error bars correspond to the standard deviation (s.d.) of three measurements (n = 3).

After analyzing the degradation products from ASR1-V265T and ASR1-W120R, **Fig. 11** revealed their effectiveness in breaking down PET film. Importantly, these degradation products encompassed not only TPA but also intermediate products like BHET and monohydroxyethyl terephthalate (MHET). Recognizing the potential challenge of mixed products for PET repolymerizarion, we
decided to pivot our attention towards the production of homogeneous TPA.

![](https://static.igem.wiki/teams/4790/wiki/e.png)

**Fig. 11** Product composition of PET depolymerization. (a) PET released monomers of ASR1. (b) PET released monomers of ASR1-V265T. (c) PET released monomers of ASR1-W120R. Reaction conditions: The PET films (ø = 6 mm) were soaked in 2000 μL of Na2HPO4-NaH2PO4 buffer (pH 8.0, 50 mM) at 30 °C, 40 °C, 50 °C and 60 °C with 100 μL of 0.5 mg/mL enzyme solution for five days. Error bars correspond to the standard deviation (s.d.) of three measurements (n = 3).

## Cycle4: Molecular docking and Molecular Dynamics simulations

### Design

In order to explain the mechanism behind the increased enzyme activity of ASR1 variants, we chose to use the MD simulation method. Software Autodock, GROMACS, and PYMOL were used respectively, for molecular docking, MD simulations and visualization analysis. PETase was used as the control group, PETase and ASR1 variants were docked to the substrate PET dimer respectively, then the conformation with the lowest binding energy was selected to construction system for MD simulations. The mechanism of increased enzyme activity was explained through the analyze of the structural observables and solvation observables.

### Build

PETase, ASR1-WT, ASR1-V265T, and ASR1-W120R were selected as the experimental subject. We used software FoldX to mutate the corresponding positions of ASR1-WT. Then, ASR1-W120R and ASR1-V265T were obtained. AutodockVina was used for molecular docking of four protein structures with PET dimer respectively, and the optimal conformation was selected as the preparation material for MD simulation. Based on the experimental system of plastic degrading enzymes, MD simulations were carried out using GROMACS software, constructing a system of enzymes, PET dimer and solvent molecules, then the system energy was minimized and a NVT and NPT ensemble equilibrium of 100 ps was performed. After the system equilibrium, a MD simulation of 100 ns was performed.

### Test

Different parameters of the simulation results were determined, obtained data such as the root-mean-square deviation (RMSD), root-mean-square fluctuation for each residue (RMSF), radius of gyration Rg, solvent-accessible surface area (SASA), the number of water molecules around the active site and the distance between the substrate and the enzyme active center, etc. Software GraphPad Prism was used to draw figures and to analyze the mechanism of increased enzyme activity.

### Learn

By analyzing the stability of PETase and ASR1 variants, it could be seen that the average RMSD values of ASR1 variants were higher than PETase at 30 °C. The RMSD value of ASR1-W120R in ASR1 variants was the least, and the value of ASR1-WT was close to ASR1-V265T. This indicated that ASR1-W120R is more stable than ASR1-WT **(Fig. 12a)**. The time-averaged total, hydrophobic, and hydrophilic SASA of PETase and ASR1 variants showed a similar trend as Rg **(Fig. 12b - c)**. It was observed that the RMSF value of PETase fluctuated greatly at different temperatures of 30 °C and 60 °C, especially a sudden rose in the range of amino acids P155-V167, which showed that PETase had larger overall fluctuation at high temperatures and higher protein flexibility **(Fig. 12d)**. The RMSF value of ASR1-W120R fluctuated very little at 30 °C and 60 °C, indicating that the variant could sustain stability even at elevated temperatures **(Fig. 12e)**. Through the analysis of PETase and ASR1 variants on multiple stability indexes, we found that ASR1-W120R could maintain considerable stability at high temperatures. Thus, further confirmed that ASR1-W120R was an excellent PET degrading enzyme.

![](https://static.igem.wiki/teams/4790/wiki/engineer121012.png)

**Fig. 12** MD simulations of stability analysis of PETase an ASR1 variants. (a) Time-average RMSD of the heavy atoms of four enzymes determined from the last 40 ns of simulation in water. (b) The radius of gyration Rg  of four enzymes with respect to the initial structure as a function of time in water. (c) The time-averaged total SASA, the time-averaged hydrophobic SASA, and hydrophilic SASA of four enzymes at temperatures of 30 °C. RMSF of each residue of (d) PETase and (e) ASR1-W120R was determined from the last 40 ns simulation at temperatures of 30 °C and 60 °C with three independent MD runs.

The solvation of the active site of an enzyme was an important factor affecting its activity. Different enzymes had different characteristics at the active site, and the binding of solvent molecules to the active site were also varied. As showed in  **Fig. 13a** , under the conditions of 30 °C and 60 °C, the number of water molecules around the substrate binding site of ASR1 variants was more than twice as much as PETase, and the number of water molecules around the substrate binding site of ASR1-W120R and ASR1-V265T was 2 to 4 more than that in ASR1-WT. This suggested that the substrate binding site of the ASR1 variants could attract more water molecules. Meanwhile, as evident from **(Fig. 13b)**, the active sites of the four enzymes kept getting closer to PET dimer in water as simulation time increased, and finally tended to be stable. When a stable state was reached, the distance between the active site in ASR1-WT and PET dimer was less than that between the active site in PETase and the PET dimer core. In addition, the distance between the active site and the PET dimer core of the two ASR1 variants was less than that of ASR1-WT. This suggested that compared with PETase, it was easier for the substrate to enter the active center of the ASR1 variants, therefore, promoting the transformation of the substrate. These results indicated that the activity of ASR1 variants designed by our team was better than PETase, and the activity of ASR1-W120R and ASR1-V265T variants was superior to ASR1-WT.

![](https://static.igem.wiki/teams/4790/wiki/engineer131012.png)

**Fig. 13**  MD simulations of activity analysis of PETase an ASR1 variants. (a) The average number of water molecules around the substrate binding site during the MD simulations at temperatures of 30 °C and 60 °C. The number of water molecules averaged over the last 40 ns from three independent MD runs. (b) The distance between the active sites of the four enzymes and the centroid of the PET dimer in water. All error bars show the standard deviation from three independent MD runs.

Through the analysis of the stability and activity of the four enzymes, it showed that the mechanism of the increased enzyme activity of the variants was that the number of water molecules around the substrate binding site increased, and the distance between the substrate and the active center was closer. The MD simulation provided theoretical support for the prediction of the protein designed by our team. Furthermore, it indicated that the two variants ASR1-V265T and ASR1-W120R had high activity and stability, and could be used for more application exploration.

## Cycle5: Constructing a Two-Enzyme Degradation System to Generate Homogeneous TPA

### Design

A mixed product was produced by a single-enzyme degradation system using PET film as a substrate in Cycle 3, which was unsuitable for further recycling. To address this challenge, we have developed a two-enzyme degradation system that combines ASR1-PETase and BHETase (BsEst, identified in our previous study).

### Build

We established a two-enzyme degradation system utilizing BsEst in combination with either ASR1-V265T or ASR1-W120R to degrade PET film at 30 °C, 40 °C, 50 °C, and 60 °C. Our results conclusively demonstrated the successful production of homogeneous TPA through this two-enzyme degradation system.

### Test

The degradation efficiency of the two-enzyme system was verified at 30-60 °C using PET film as substrate, and the content of homogeneous TPA in the reaction solution was measured by HPLC after 120 h of reaction.

![](https://static.igem.wiki/teams/4790/wiki/engineeringfourth.png)

**Fig. 14** HPLC data of concentration of TPA in the reaction system after 120 h of degradation by the two-enzyme system. Reaction conditions: The PET films (ø = 6 mm) were soaked in 2000 μL of 50 mM Na2HPO4-NaH2PO4 (pH 8.0) buffer at 30 °C, 40 °C, 50 °C, 60 °C with 100 μL of 0.5 mg/mL enzyme solution for five days. Error bars correspond to the standard deviation (s.d.) of three measurements (n=3).

### Learn

**Fig. 14** highlights two key advantages of this two-enzyme system. Firstly, compared to single enzyme system, the two-enzyme system substantially enhances the efficiency of PET film degradation. The combination of ASR1-V265T and ASR1-W120R with BHETase (BsEst) resulted in a remarkable increase in TPA concentration by 1.7-fold and 1.8-fold, respectively. Secondly, the two-enzyme degradation system exclusively produces homogeneous TPA.

## Cycle6: PET Repolymerization for the Closed-Loop PET Recycling

### Design

To achieve the closed-loop PET recycling, we designed a chemical-enzymic approach based on the homogeneous TPA through the two-enzyme system.

### Build

Initially, PET waste was degraded into the homogeneous TPA using the two-enzyme system. The homogeneous TPA was then synthesized into Dimethyl Terephthalate (DMT) under specified H2SO4/CH3OH chemical conditions. Finally, DMT was directly polymerized with Ethylene Glycol (EG) and Ti(Oi-Pr)4 to regenerate virgin PET.

### Test

Following this chemical-enzymic approach **(Fig. 15)**, roughly 298 mg of PET was obtained from 654 mg of TPA, and the PET crystallinity was approximately 2.30%, which indicated that we achieved the closed-loop PET recycle.

![](https://static.igem.wiki/teams/4790/wiki/engineering15.png)

**Fig. 15** Closed-Loop PET Recycling.

### Learn

The successful depolymerization and repolymerization of PET reaffirm our confidence in the potential of PETase and monomeric TPA beyond PET recycling. We firmly believe that this approach has the capacity to revolutionize conventional PET degradation methods, mitigating environmental pollution and reducing the need for initial TPA production from petroleum. Such advancements hold significant promise in various domains, including plastic enhancement and recycling, as well as the degradation of human microplastics, contributing to a more sustainable future.

## Reference

1. Ali, S. S.; Elsamahy, T.; Al-Tohamy, R.; Zhu, D.; Mahmoud, Y. A. G.; Koutra, E.; Metwally, M. A.; Kornaros, M.; Sun, J., Plastic wastes biodegradation: Mechanisms, challenges and future prospects. *Science of the Total Environment* **2021**, *780*, 146590.

2. Guzzetti, D.; Lebrun, A.; Subileau, M.; Grousseau, E.; Dubreucq, E.; Drone, J., A Catalytically Competent Terpene Synthase Inferred Using Ancestral Sequence Reconstruction Strategy. *ACS Catalysis* **2016**, *6* (8), 5345-5349.

3. Gricajeva, A.; Nadda, A. K.; Gudiukaite, R., Insights into polyester plastic biodegradation by carboxyl ester hydrolases. *Journal of Chemical Technology and Biotechnology* **2022**, *97* (2), 359-380.

4. Roussel, A.; Amara, S.; Nyyssola, A.; Mateos-Diaz, E.; Blangy, S.; Kontkanen, H.; Westerholm-Pantinen, A.; Carriere, F.; Cambillau, C., A Cutinase from Trichoderma reesei with a Lid-Covered Active Site and Kinetic Properties of True Lipases. *Journal of Molecular Biology* **2014**, *426* (22), 3757-3772.

5. Chen, C. C.; Dai, L.; Ma, L.; Guo, R. T., Enzymatic degradation of plant biomass and synthetic polymers. *Nature Reviews Chemistry* **2020**, *4* (3), 114-126.

6. Yoshida, S.; Hiraga, K.; Takehana, T.; Taniguchi, I.; Yamaji, H.; Maeda, Y.; Toyohara, K.; Miyamoto, K.; Kimura, Y.; Oda, K., A bacterium that degrades and assimilates poly(ethylene terephthalate). *Science* **2016**, *351* (6278), 1196-1199.

7. Fu, Y.; Wang, X.; Dong, H.; Jiang, Y. G.; Wang, M.; Xue, X.; Sigal, L., Vocabulary-Informed Zero-Shot and Open-Set Learning. *Ieee Transactions on Pattern Analysis and Machine Intelligence* **2020**, *42* (12), 3136-3152.

8. Meier, J.; Rao, R.; Verkuil, R.; Liu, J.; Sercu, T.; Rives, A. In Language models enable zero-shot prediction of the effects of mutations on protein function, 35th Conference on Neural Information Processing Systems (NeurIPS), Electr Network, **2021**.

9. Hopf, T. A.; Ingraham, J. B.; Poelwijk, F. J.; Scharfe, C. P. I.; Springer, M.; Sander, C.; Marks, D. S., Mutation effects predicted from sequence co-variation. *Nature Biotechnology* **2017**, *35* (2), 128-135.

10. Hsu, C.; Nisonoff, H.; Fannjiang, C.; Listgarten, J., Learning protein fitness models from evolutionary and assay-labeled data. *Nature Biotechnology* **2022**, *40* (7), 1114.

11. Hie, B. L. L.; Shanker, V. R. R.; Xu, D.; Bruun, T. U. J.; Weidenbacher, P. A. A.; Tang, S.; Wu, W.; Pak, J. E. E.; Kim, P. S. S., Efficient evolution of human antibodies from general protein language models. *Nature Biotechnology* **2023**, *10*, 1038.

12. Shashkova, T. I. I.; Umerenkov, D.; Salnikov, M.; Strashnov, P. V. V.; Konstantinova, A. V. V.; Lebed, I.; Shcherbinin, D. N. N.; Asatryan, M. N. N.; Kardymon, O. L. L.; Ivanisenko, N. V. V., SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning. *Frontiers in Immunology* **2022**, *13*, 960985.

13. Cui, H.; Vedder, M.; Schwaneberg, U.; Davari, M. D. Using molecular simulation to guide protein engineering for biocatalysis in organic solvents. *Methods Mol Biol*  **2022**, 2397, 179– 202.

14. Jerves, C.; Neves, R. P. P.; Ramos, M. J.; da Silva, S.; Fernandes, P. A., Reaction Mechanism of the PET Degrading Enzyme PETase Studied with DFT/MM Molecular Dynamics Simulations. *ACS Catalysis* **2021**, 11 (18), 11626-11638.

15. Yin, Q.; You, S.; Zhang, J.; Qi, W.; Su, R., Enhancement of the polyethylene terephthalate and mono-(2-hydroxyethyl) terephthalate degradation activity of Ideonella sakaiensis PETase by an electrostatic interaction-based strategy. *Bioresour Technology* **2022**, 364, 128026.

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##### Ⅰ. Seamless cloning and construction

1. Design primers to linearize pET-22b(+) vector through PCR.
2. Ligation between node 59 and linearized pET-22b(+) vector is incubated for 30 min at 50 °C.
3. Add 10 µL reaction mix to 100 µL of *Escherichia coli* DH5⍺ competent cells and rest for 30 min.
4. Coat on LB plates after heat shock for 60 sec.

##### Ⅱ. Construction of ASR1-PETase variants

Variants were generated through site-directed mutagenesis using PCR method.
1. Standard PCR reaction
Make reaction mix (Total reaction volume: 50 μL)

Table acquired from Code No (takarabiomed.com.cn)

| Component | Amount | Final Concentration |
| PrimeSTAR Max Premix（2X） | 25 μl | 1 X |
| Prime 1 | 10 ～ 15 pmol | 0.2～0.3 μM |
| Prime 2 | 10 ～ 15 pmol | 0.2～0.3 μM |
| Template | < 200 ng |  |
| ddH2O | w μL | up to 50 μL |

2. Implement the process of two-step PCR
Mix the reaction mixture by vortexing and briefly centrifuge to collect any drops on the side of the tube.
1). Predenature templates 98 ℃ for 5 min.
2). Amplification cycles are as below:

| PCR step | Temperature °C | Duration |
| denature | 98 °C | 15 sec |
| Anneal primers | 60 °C /55 °C | 15 sec/5 sec |
| Extension | 72 °C | 4 min |

3). For the first step in two-step PCR, forward primer and downstream primer are added to two PCR tubes respectively. The first step needs to implement 3 cycles.
4). For the second step in two-step PCR, mix PCR reaction solution of the corresponding forward primer and downstream primer and implement for 15 cycles.
5). Both steps need to extend for another 10 min after implementing cycles and store at 4 °C.

3. Validate PCR products through nucleic acid electrophoresis
1). Dissolve 1 g agarose in 100 mL 1XTAE to make agarose gel
2). Mix PCR reaction samples with 10X loading buffer
3). Load samples and marker
4). Run at 120 V for 20 min
5). Image the gel with a gel imager

4. Digest PCR reaction solution with DPNⅠ for 3 h at 37 °C

##### Ⅲ. Transform

Transform competent DH5⍺
1. Thaw a tube of DH5⍺ cells on ice for 5 min.
2. Pipette 10 μL digested PCR reaction solution into the tube. Flick the tube carefully for 6 - 8 times.
3. Plug the tube into ice for 30 min.
4. Heat shock for exactly 90 sec at exactly 42 °C.
5. Plug the tube into ice for another 2 min.
6. Pipette 100 μL of the cell mixture onto LB + Amp plate and spread.
7. Cultivate overnight at 37 °C.
8. Select two isolated colonies for each mutation site
9. Make liquid cultures of selected colonies and cultivate for 12 - 14 h at 37 °C in shaking incubator.
10. Send the broth to the sequencing company (Tsingke Biotechnology Co., Ltd. (Nanjing, China)) and use Snapgene to make sequence comparison.

##### Ⅳ. The recombinant plasmids transformed into *Escherichia coli* BL21 (DE3)

1. Thaw a tube of BL21 cells on ice for 5 min.
2. Pipette 10 μL (50 ng/L) plasmids (extracted from DH5⍺ cells) into the tube. Flick the tube carefully for 6 - 8 times.
3. Plug the tube into ice for 30 min.
4. Heat shock for exactly 90 sec at exactly 42 °C.
5. Plug the tube into ice for another 2 min.
6. Pipette 800 μL liquid LB medium with no antibiotic.
7. Cultivate for 1 h at 37 °C 220 r.p.m. in shaking incubator.
8. Centrifuge the tube for 2 min.
9. Discard 800 μL supernatant and resuspend bacteria with the rest supernatant.
10. Pipette 100 μL of the cell mixture onto LB + Amp plate and spread.
11. Cultivate overnight at 37 °C.

Incubate weed stock
1. Select two isolated colonies for each mutation site from the plate.
2. Make liquid cultures of selected colonies and cultivate for 12 h at 37 °C.
3. Store weed stock at -80 °C.

##### Ⅴ. Protein expression

1. Pipette 2 mL weed stock and add the weed stock into a flask containing 100 mL liquid LB with antibiotic.
2. Add appropriate amount of isopropyl-1-thio-β-D-galactopyranoside (IPTG) to reach final concentrations of 0.1 mM IPTG, when the optical density of cultures at 600 nm (OD600) reached 0.4–0.6.
3. Cultivate the bacteria at 20 °C 150 r.p.m. for 24 h.

Protein extraction
1. Prepare enough sterile 50 mL centrifuge tubes to hold bacteria broth.
2. Centrifuge fermented bacteria broth at 8000 r.p.m. for 10 min.
3. Repeat step 1 till flasks are empty.
4. Add 30 mL PBS (50 mM Na2HPO4-NaH2PO4 buffer, pH 8.0) into each tube.
5. Centrifuge at 8000 r.p.m. for 10 min.
6. Add 20 mL PBS (pH 8.0) into each tube and resuspend bacteria.
7. Sonicate with Intelligent Ultrasonic Processor for 25 min (3 sec on, 5 sec off, AMP 35%) to break cells.
8. Sonicate the cell slurry until it becomes transparent, indicating most cells have lysed.
9. Centrifuge sonicated cell suspension at 8000 r.p.m. for 30 min, at 4 °C.
10. Verify the supernatant through sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and store the supernatant at -20 °C.

##### Ⅵ. Verify the enzymatic effect of all 20 variants toward BHET

Use BHET as the only substrate and react at 30 °C and 50 °C.
1. Dissolve appropriate amount of BHET (molecular weight: 254.24) in PBS (pH 8.0) to achieve concentration of 5 mM BHET.
2. Add 2 mL solution prepared in above step into each well of a 24-well well plate.
3. Pipette 100 μL enzyme extracted from each variant (step Ⅴ)
4. Place the well plate on a shaking table at 30 °C and 50 °C for 6 h.
5. Take samples
6. Filter altogether 400 μL reaction solution with organic filter membrane (0.22 μm).
7. Analyze the samples with HPLC.
8. All the experiments are conducted in triplicate.

##### Ⅶ. Verify the enzymatic effect of selected variants toward PET film

Use PET film as a substrate and react at 30 °C, 40 °C, 50 °C and 60 °C respectively.
1. Add 2 mL PBS and a PET film (ø = 6 mm) into each well of a 24-well plate.
2. Pipette 100 μL enzyme extracted from selected variants.
3. Add 100 μL enzyme every 24 h, the whole reaction lasting for 5 d.
4. Take samples in the third and fifth day (specific steps in step Ⅵ).
5. Analyze the samples with HPLC.
6. Two variants were selected through statistical normative scores by assigning weights of 10%, 20%, 30%, and 40% to 30 °C, 40 °C, 50 °C, and 60 °C.

##### Ⅷ. Construct two-enzyme system to degrade PET film

All the experimental steps in this session are completely the same as the session above (Ⅶ), but we added BHETase (BsEst) to construct a two-enzyme system to test the degradation effect. The results are presented in the form of TPA concentration and homogeneous TPA is used in PET recycling.

##### Ⅸ. PET recycling

1. Filter the enzyme degradation solution and collect the filtrate for regeneration.
2. Add appropriate amount of 37% HCl to adjust the pH of the solution to 2.
3. Filter the filtrate and wash with deionized water for several times.
4. Dry under vacuum overnight.
5. Dissolve TPA obtained from chemical-enzymatic catalysis in CH3OH at room temperature.
6. Add 95% H2SO4 to the suspension and stir the mixture under reflux for 24 h.
7. Recrystallize the reaction mixture from dimethyl terephthalate (DMT) and wash with CH3OH.
8. Dry under vacuum to obtain white powder and analyze the product (DMT) with LC-MS.
9. Add DMT (1.39 g) to the vacuum reactor with EG (1.20 mL) and titanium isopropoxide (0.04 mL).
10. Stir the reactor at 160 °C, 200 °C, 210 °C, 260 °C for 1 h, 1 h, 2 h, 2 h, respectively.
11. Dissolve the resulting PET in 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP, 10 mL) and add dropwise to CH3OH to remove the catalyst.
12. Centrifuge the solution, dry under vacuum, and collect PET as a white solid.

## Supplementary Workflows

##### Ⅰ. Prepare liquid LB

10 g NaCl + 10 g tryptone + 5 g yeast extract + 1 L deionized water
(Add 2% agar to make LB agar plate)

##### Ⅱ. Prepare analytical agarose gel

1. Add 1 g of analytical agarose and 100 mL 1XTAE into the flask which is only used to prepare analytical agarose gel.
2. Microwave until all the agarose is completely dissolved.
3. Cool the flask until its temperature reach 55 °C
4. Pipette 10 μL nucleic acid dye and shake it evenly.
5. Pour mixture into gel mold.

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* [1. The Ancestral Sequence Reconstruction of PETase](#1. The Ancestral Sequence Reconstruction of PETase)
* [2. A Machine Learning-guided Strategy to Engineer ASR1-PETase](#2. A Machine Learning-guided Strategy to Engineer ASR1-PETase)
* [3. The High Thermostability Variants by Site-directed Mutagenesis](#3. The High Thermostability Variants by Site-directed Mutagenesis)
* [4. Mechanism Analysis of Increased Enzyme Activity by MD Simulations](#4. Mechanism Analysis of Increased Enzyme Activity by MD Simulations)
* [5. Complete TPA Conversion of PET Achieved by A Simple Two-Enzyme System](#5. Complete TPA Conversion of PET Achieved by A Simple Two-Enzyme System)
* [6. Homogeneous TPA Repolymerization PET to Achieve the Closed-loop PET Recycling](#6. Homogeneous TPA Repolymerization PET to Achieve the Closed-loop PET Recycling )
* [7. Disscusion](#7. Disscusion)

## **1. The Ancestral Sequence Reconstruction of PETase**

Recently, some researchers have suggested that profile-HMM algorithm could be used instead of BLAST local alignment algorithm for protein sequences in silico in order to circumvent low overall sequence similarity between them. Using the Pfam entry PF01738 (dienelactone hydrolase family) and the HMMER Web server to search Swiss-prot and NCBI public databases, we obtained 158 protein sequences from all species. Expected values (E-value) were between 2.4 × 10–76 and 0.47. Lower E thresholds are more stringent, leading statistically to fewer chance-matches being reported. The 50 first bacterial protein sequences (2.4 × 10–76 ≤ E ≤ 2.9 × 10–6) were selected and we deleted three duplicate records. Among these protein candidates, 39 proteins have dienelactone hydrolase (DLH) domains. 21 homologous sequences were then manually added for ancestral sequence reconstruction (ASR). These sequences were aligned, and the corresponding phylogenetic tree was built using a maximum likelihood algorithm by Fireprot server **(Fig. 1a)**.

We conducted an evolutionary analysis of PETase based on the phylogenetic tree **(Fig. 1a)**. A lot of studies have confirmed that the certain microorganisms can secrete the extracellular enzyme to degrade biomass to support their growth and metabolism. Tracing back to the ancestral era of plastic-degrading enzymes, the α/β hydrolase superfamily differentiated into hydrolases with distinct functions and α/β structures at a certain stage (ASR3-PETase). The hallmark of α/β hydrolase folding is its remarkable plasticity, ensuring the multifunctionality of the superfamily enzymes, including lipases and esterases/cutinases, and their versatility with respect to various substrates. For example, one branch of these enzymes, the cutinases, is capable of hydrolyzing ester bonds, thus breaking down the keratin structures in plants. Nature's wonder lies in its diversity, as beyond the α/β hydrolases, certain enzymes have been endowed with peroxidase or acyl-coenzyme A (CoA) activities, such as ACOT4 HUMAN and SERINE ESTERASE, which can act on phenolic groups found in lignin or remove acetyl side chains. With the development of society, plastic became widely used as a new artificial material. Due to the structural similarity between plastics and keratin or lignin, cutinases and ligninases evolved into more effective plastic-degrading enzymes, such as PETase. Next, we sought to evaluate the PET-hydrolytic activity of reconstructed ancestral sequence (ASR1-7). As shown in **Fig. 1b**, ASR1-PETase and ASR7-PETase exhibited high activity in degrading PET, releasing monomer quantities of 3.44 mM and 3.82 mM, respectively. Compared with the wild type PETase, the identity of ASR1-PETase (21.37%) significantly lower than that of ASR7-PETase (67.25%) **(Fig. 1c)**.

From the perspective of natural evolution, the evolutionary distance from ASR7-PETase to PETase was relatively close, with a limited variant space. Conversely, due to a longer evolutionary distance, obvious identity difference was observed between ASR1-PETase and PETase. For the structural analysis of PETase and ASR1-PETase, we found that both PETase and ASR1-PETase possessed the canonical α/β hydrolase domain, the conserved catalytic triad, and disulfide bonds that facilitated the stability of enzyme structure and function **(Fig. 1d)**. Therefore, ASR1-PETase was selected as protein scaffold for further evolution through a machine learning-guided strategy.

![](https://static.igem.wiki/teams/4790/wiki/zuzhe1012.png)

**Fig. 1** Evolutionary analysis of PETase on the basis of reported PET hydrolases. (a) A phylogenetic tree constructed on the basis of Swiss-prot and NCBI databases employing FireProt (https://loschmidt.chemi.muni.cz/fireprotweb/). Number 1-7 represents ASR1 (node 59), ASR2 (node 98), ASR3 (node 100), ASR4 (node 109), ASR5 (node 111), ASR6 (node 112), and ASR7 (node 115). (b) PET depolymerization by ASR1-7. Reaction conditions: The PET films (ø=6 mm) were soaked in 2000 μL of Na2HPO4-NaH2PO4 (pH 8.0, 50 mM) buffer at 40 °C with 100 μL of 0.5 mg/mL enzyme solution for five days. Error bars correspond to the standard deviation (s.d.) of three measurements (n = 3). (c) Sequence identities between wide type PETase and ASR1-7. (d) 3D structures of PETase and ASR1-PETase. Red sticks represent the catalytic triad (S131-D177-H208 of PETase and C161-D210-H242 of ASR1-PETase).

## **2. A Machine Learning-guided Strategy to Engineer ASR1-PETase**

During the protein application, it is necessary to improve the target protein to meet industrial conditions. The emergence of directed evolution has circumvented some restrictions to some extent, guiding proteins to evolve in a specific preset direction through random mutation and screening, but the applicability and flux limitations of directed evolution screening systems often become the bottleneck of complex protein engineering. To address this challenge, machine learning algorithm was more effective than experimental evolution. Modeling the mutation-function relationship of proteins helps to understand and target protein design, and ESM-1v is an effective machine learning model. ESM-1v can predict protein substitution even when there are few experiments or when relevant information is missing. By fine-tuning the protein family sequence model, we can find the key sites associated with mutations and efficiently predict the effects of variants on protein function. The ESM-1v model is universal and can be used to predict multiple needs without model specialization for each prediction **(Fig. 2a)**. Comparison of ESM-1v with earlier models (such as Evmutation, DeepSequence) were shown in **Table 1**.

As shown in **(Fig. 2c)**, the heat map of the replacement of sequence sites will be obtained after employing ESM-1v model, and the potential variants could be obtained according to the heat map. We select top 20 variants (including P177E, K249E, I223E, W120R, Q79N, S242G, L246H, Y46I, N81H, G201A, F213S, F275L, L122K, C287P, M17A, V265T, P177E, I223E, W120R, Q79N, S242G, L246H, Y46i, N81H, G201A, F213S, F275L, L122K, C287P. C26A, E125R, M40E, Y6A)with the highest scores, and PET-hydrolytic activity was detected through site-directed mutagenesis **(Fig. 2b)**.

![](https://static.igem.wiki/teams/4790/wiki/qaqaaaaxsc.png)

**Fig. 2** Machine learning （ESM-1v）guided predictions improve enzyme performance using ASR1 scaffolds. (a) Comparison of algorithms for ESM-1v, Evmutation, and Deepsequence. (b) Potential positions of ASR1-PETase by machine learning. The red sticks represent the catalytic triad, and the blue sticks represent the variant position. (c) The schematic of ESM-1v algorithm.

**Table 1** The comparison of the machine learning model

| Model peculiarity | Desired parameter | Unsupervised learning algorithm | Apply the Zero-Shot method | Susceptible to interference | Predict specific protein properties that are not affected by long-term evolutionary selection | Multiple sequence alignments (MSA) need to be generated in order to calculate mutations |
| ESM-1v | 6-7 times less than the traditional algorithm | ⚪ | ⚪ | × | ⚪ | × |
| Traditional model | A good many | × | × | ⚪ | × |  |
| EVmutation | less | ⚪ | × | ⚪ | × | ⚪ |
| DeepSequence | less | ⚪ | × | ⚪ | × | ⚪ |

## **3. The High Thermostability Variants by Site-Directed Mutagenesis**

In our study, we designed 20 variants to investigate the thermostability and catalytic efficiency. As shown in **Fig. 3a-b**, the Bis (2-Hydroxyethyl) terephthalate (BHET) conversion of both ASR1-PETase and its variants was generally higher than that of the wild type PETase. Among them, four variants ASR1-V265T, ASR1-N81H, ASR1-W120R, and ASR1-L122K exhibited increased BHET conversion at both 30 °C and 50 °C, high up to 2.71-fold of ASR1-PETase. Meanwhile, the corresponding terminal product Terephthalic acid (TPA) of four variants, was also maintained at high yields (2.58 to 2.88 mM). Furthermore, hydrolysis advantages of ASR1-V265T, ASR1-N81H, ASR1-W120R, and ASR1-L122K were also presented on PET film. As depicted in **Fig. 3c**, the ASR1-PETase variants maintained hydrolysis activity at 60 °C, whereas most of the wild type PETase activity was lost over 30 °C. In details, these four variants exhibited improved catalytic efficiencies on PET film at 40 °C, up to 1.58-fold (ASR1-V265T). Additionally, it was observed that the variants ASR1-V265T and ASR1-W120R maintained PET degradation products at levels of 2.40 mM and 2.69 mM, respectively, at a temperature of 60 °C, exhibiting an average increase of 2.32-fold compared to ASR1-PETase. Considering the necessity of high thermostability for industrial enzymes, we adopted a scoring strategy to identify variants with greater thermostability. We assigned different weights to the data obtained at 30 °C, 40 °C, 50 °C, and 60 °C, which were 10%, 20%, 30%, and 40%, respectively, to determine the composite scores. Based on the final scores, ASR1-V265T (Score 4.258) and ASR1-W120R (Score 3.978) was selected to analyze the product composition **(Fig. 3d-f)**. We found that the terminal product TPA accounted for 63% to 75% of the total PET released monomers, while the remaining portion mainly comprised intermediates BHET and monohydroxyethyl terephthalate (MHET). These results indicated that enzymatic degradation of PET by a single enzyme will generate inhomogeneous products, resulting in contamination of the terminal product TPA. Considering that mixed products are unfavorable for subsequent reuse, we shifted our focus to the production of homogeneous TPA.

![](https://static.igem.wiki/teams/4790/wiki/result31012.png)

**Fig. 3** Experimental verification of the variants upon BHET and PET film. 20 variants were validated through BHET experiments, including (a) conversion of BHET and (b) concentration of TPA. Reaction conditions: The BHET (5 mM) was dissolved in 2000 μL Na2HPO4-NaH2PO4 buffer (pH 8.0, 50 mM) at 30 °C and 50 °C with 100 μL crude enzyme solution for 6 h. Error bars correspond to the standard deviation (s.d.) of three measurements (n = 3). (c) Product released from PET depolymerization by ASR1-V265T, ASR1-N81H, ASR1-W120R and ASR1-L122K. Score = C30 °C\*10% + C40 °C\*20% + C50 °C\*30% + C60 °C\*40%. Product composition of PET depolymerization by (d) ASR1, (e) ASR1-V265T, and (f) ASR1-W120R. Reaction conditions: The PET films (ø = 6 mm) were soaked in 2000 μL of Na2HPO4-NaH2PO4 buffer (pH 8.0, 50 mM) at 30 °C, 40 °C, 50 °C and 60 °C with 100 μL of 0.5 mg/mL enzyme solution for five days. Error bars correspond to the standard deviation (s.d.) of three measurements (n = 3).

## **4. Mechanism Analysis of Increased Enzyme Activity by MD Simulations**

By analyzing the stability of PETase and ASR1 variants, it could be seen that the average RMSD values of ASR1 variants were higher than PETase at 30 °C. The RMSD value of ASR1-W120R in ASR1 variants was the least, and the value of ASR1-WT was close to ASR1-V265T. This indicated that ASR1-W120R is more stable than ASR1-WT  **(Fig. 4a)**. The time-averaged total, hydrophobic, and hydrophilic SASA of PETase and ASR1 variants showed a similar trend as Rg   **(Fig. 4b-c)**. It was observed that the RMSF value of PETase fluctuated greatly at different temperatures of 30 °C and 60 °C, especially a sudden rose in the range of amino acids P155-V167, which showed that PETase had larger overall fluctuation at high temperatures and higher protein flexibility  **(Fig. 4d)**. The RMSF value of ASR1-W120R fluctuated very little at 30 ℃ and 60 ℃, indicating that the variant could sustain stability even at elevated temperatures  **(Fig. 4e)**. Through the analysis of PETase and ASR1 variants on multiple stability indexes, we found that ASR1-W120R could maintain considerable stability at high temperatures, thus, further confirmed that ASR1-W120R was an excellent PET degrading enzyme.

The solvation of the active site of an enzyme was an important factor affecting its activity. Different enzymes had different characteristics at the active site, and the binding of solvent molecules to the active site were also varied. As showed in  **Fig. 4f**, under the conditions of 30 °C and 60 °C, the number of water molecules around the substrate binding site of ASR1 variants was more than twice as much as PETase, and the number of water molecules around the substrate binding site of ASR1-W120R and ASR1-V265T was 2 to 4 more than that in ASR1-WT. This suggested that the substrate binding site of the ASR1 variants could attract more water molecules. Meanwhile, as evident from  **(Fig. 4g)**, the active sites of the four enzymes kept getting closer to PET dimer in water as simulation time increased, and finally tended to be stable. When a stable state was reached, the distance between the active site in ASR1-WT and PET dimer was less than that between the active site in PETase and the PET dimer core. In addition, the distance between the active site and the PET dimer core of the two ASR1 variants was less than that of ASR1-WT. This suggested that compared with PETase, it was easier for the substrate to enter the active center of the ASR1 variants, therefore, promoting the transformation of the substrate. These results indicated that the activity of ASR1 variants designed by our team was better than PETase, and the activity of ASR1-W120R and ASR1-V265T variants was superior to ASR1-WT.

![](https://static.igem.wiki/teams/4790/wiki/result41012.png)

**Fig. 4**  Overall structural change and solvation phenomena around the whole enzymes of PETase and ASR1 variants in water. (a) Time-average RMSD of the heavy atoms of four enzymes determined from the last 40 ns of simulation in water. (b) The radius of gyration (Rg) of four enzymes with respect to the initial structure as a function of time in water. (c) The time-averaged total SASA, the time-averaged hydrophobic SASA, and hydrophilic SASA of four enzymes at temperatures of 30 °C. RMSF of each residue of (d) PETase and (e) ASR1-W120R was determined from the last 40 ns simulation at temperatures of 30 °C and 60 °C with three independent MD runs. (f) The average number of water molecules around the substrate binding site during the MD simulations at temperatures of 30 °C and 60 °C. The number of water molecules averaged over the last 40 ns from three independent MD runs. (g) The distance between the active sites of the four enzymes and the centroid of the PET dimer in water. All error bars show the standard deviation from three independent MD runs.

## **5. Complete TPA Conversion of PET Achieved by A Simple Two-Enzyme System**

TPA produced by a single-enzyme degradation system often suffered from contamination by oligoethylene terephthalates, BHET, and MHET, which posed limitations on downstream applications. To overcome the obstacle, we have developed a two-enzyme degradation system that combines ASR1-V265T and ASR1- W120R, both performing better in preliminary validation and MD simulations, with BHETase (BsEst, identified in our previous study). It was proved that the system enables the production of sole homogeneous TPA, as illustrated in  **Fig. 5a**. Coupling BsEst with two variants of ASR1 respectively in a two-enzyme system significantly increased TPA production compared to using variants alone in a single-enzyme system. As depicted in  **Fig. 5b**, ASR1-V265T/BsEst and ASR1-W120R/BsEst increased TPA production by 1.7-fold and 1.8-fold over 120 h. Moreover, we observed that these two-enzyme systems produced 1.9-fold and 2.3-fold higher amounts of homogeneous TPA at 30 °C than at 60 °C.

The discovery mentioned above was further supported by the analysis of the PET film surface using scanning electron microscopy (SEM), and the measurement of reduced the water contact angle  **(Fig. 5c)**. Notably, the two-enzyme system exhibited a remarkable efficiency degrading the PET film with up to a 15.6 °C water contact angle decrease in average at 60 °C, resulting in a visibly rougher surface compared to the single-enzyme system. To implement our team's sustainability philosophy and address the additional energy costs associated with high temperatures, we also investigated the PET degradation efficiency of the two-enzyme system at room temperature (30 °C) to achieve a more environmentally friendly process. We surprisingly observed better effects at room temperature compared to 60 °C. Specifically, the average reduction in water contact angle is 23.7° at 30 °C, 1.5 fold higher than at 60 °C.

![](https://static.igem.wiki/teams/4790/wiki/resultsfigfive.png)

**Fig. 5**  A two-enzyme degradation system at 30-60 °C. (a) An overview of the two-enzyme degradation system. Icon graphics of this figure was created by BioRender.com. (b) HPLC data of homogeneous TPA. Reaction conditions: The PET films (ø = 6 mm) were soaked in 2000 μL of 50 mM Na2HPO4-NaH2PO4 (pH 8.0) buffer at 30 °C, 40 °C, 50 °C, 60 °C with 100 μL of 0.5 mg/mL enzyme solution for five days. Error bars correspond to the standard deviation (s.d.) of three measurements (n = 3). (c) HPLC data of homogeneous TPA in the reaction system after 120 h of degradation by the two-enzyme system. (d) The SEM images (up panel) and water contact angle analysis (down panel) of the PET film in two-enzyme degradation system with BsEst after 48 h at 60 °C.

## **6. Homogeneous TPA Repolymerization PET to Achieve the Closed-loop PET Recycling**

To achieve the closed-loop PET recycling, we designed a chemical-enzymic approach based on the homogeneous TPA through the two-enzyme system  **(Fig. 6a)** . Initially, PET waste was degraded into the homogeneous TPA using the two-enzyme system. The homogeneous TPA was then synthesized into Dimethyl Terephthalate (DMT) under specified H2SO4/CH3OH chemical conditions. Finally, DMT was directly polymerized with Ethylene Glycol (EG) and Ti(Oi-Pr)4 to regenerate virgin PET.

Following this chemical-enzymic approach, the closed-loop recovery of PET from depolymerization to reaggregation was completed within approximately 57 h  **(Fig. 6a)** , roughly 298 mg of PET was obtained from 654 mg of TPA. The PET was collected as a white solid after centrifugation and dried under vacuum. The crystallinity of virgin PET was detected by DSC. The heat of fusion ΔHm and cold crystallization ΔHc were determined by integrating areas (J g−1) under peaks in DSC detection. The percent crystallinity was calculated using the following equation:

Crystallinity (%) =(ΔHm-ΔHc)/ΔHm×100

ΔHm is the enthalpy of melting (J g−1), ΔHc is the enthalpy of cold crystallization (J g−1), and ΔHm is the enthalpy of melting for a 100% crystalline PET sample, which is 140.1 J g−1.  **Fig. 6b**  indicated that the PET crystallinity was approximately 2.30%. These results confirmed that we had successfully synthesized PET by using the homogeneous TPA obtained by the two-enzyme system, and achieved the closed-loop PET recycling.

![](https://static.igem.wiki/teams/4790/wiki/sixxx.png)

**Fig. 6**  Closed-loop recycling PET. (a) The schematic of the closed-loop PET recycling. (b) DSC trace of PET regenerated from the degraded solutions. The crystallinity of this regenerated PET is 2.30%. The melting onset is 235.16 °C. The melting peak temperature is 248.64 °C.

## **7. Discussion**

PET biodegradation represents a sustainable, low-energy solution for PET recycling, especially compared to current disposal routes such as landfill and incineration. Recent protein engineering and the more than 70 crystal structures of bacterial PET-degrading enzymes have improved our understanding of the enzyme degradation process, but there are remained challenge about the balance between high thermostability and catalytic efficiency. This project proposes to start from the ancestor sequence of PETase and deeply explore the evolution of PET, meanwhile, to provides the skeleton for the protein engineering design of PETase. Inspired by the achievements of artificial intelligence in solving the field of protein fitness to detect hidden evolutionary information, we adopted the machine learning ESM-1v model and redesigned PETase (ASR1-V265T and ASR1-W120R). Compared with wild-type ASR1-PETase, the four variants exhibited improved catalytic efficiencies on PET film at 40 °C, up to 1.58-fold (ASR1-V265T). Additionally, it was observed that the variants ASR1-V265T and ASR1-W120R maintained PET degradation products at levels of 2.40 mM and 2.69 mM, respectively, at a temperature of 60 °C, exhibiting an average increase of 2.32-fold compared to ASR1-PETase. After MD simulations, the mechanism of promoted enzyme performance has been proofed, emphasizing that the number of water molecules around the substrate binding site increased, and the distance between the substrate and the active center was closer. At the same time, the two-enzyme system constructed in this study provides homogeneous TPA for subsequent PET repolymerization, enabling the closed-loop PET recycle, and provides guidance for further investigation of other mass-produced polymer in this interesting research field.

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