Parts overview

We designed 64 parts for our project, consisting of 36 basic parts and 28 composite parts, including the vector plasmids used in experiments, DNA sequences successfully mutated through ESM-1v, and primers used for mutagenesis. Our favorite parts are BBa_K4790033 and BBa_K4790061, which encode the most thermally stable PETase we could obtain at present. All parts we used have been submitted to the iGEM Registry according to BioBricks assembly standards. The following is brief tables of our registered parts. For more information about our parts, please visit our Parts Page.

Basic parts

Composite Parts

Improvement of PET Hydrolase

Polyethylene terephthalate (PET) is synthesized by the transesterification of dimethyl terephthalate with ethylene glycol or synthesis of dihydroxyethyl terephthalate by esterification of terephthalic acid with ethylene glycol, and then a polyester prepared by polycondensation reaction. Due to the excellent physicochemical properties and economic viability, plastic quickly conquered several industrial sectors, including packaging, healthcare, fisheries, and agriculture, over the 20th and 21st centuries. Unfortunately, the appealing attributes, stability, and low-cost of plastics have resulted in one of the most over-encumbering contemporary anthropogenic problems: plastic waste and pollution. In 2016, the discovery of Ideonella sakaiensis-derived PET hydrolase (PETase) provided a new perspective for biocatalytic degradation of plastic waste.

2022 TAMU iGEM tackled the critical challenge of plastic pollution, specifically targeting PET materials. The enzymes PETase and MHETase, produced naturally by soil bacterium Ideonella sakaiensis, work synergistically to degrade PET into environmentally benign monomers, terephthalic acid and ethylene glycol. Their aim for the project is to achieve surface display of PETase (BioBrickBBa_{_}K4484000) and MHETase separately in Escherichia coli in order to create whole-cell biocatalysts with the ability to break down PET. Their work with MHETase and PETase combined with various anchor proteins, such as AIDA-I, Ice Nucleation Protein, and YeeJ, and they are used to bring plastic degrading enzymes to the surface of Escherichia coli cells. Their results in Fig. 1a showed that the wild type PETase exhibits poor durability: most of its activity is lost after incubation at 37 ^°C. Additionally, the heterogeneous products (mixture containing BHET, MHET, and TPA) were yielded from PET degradation by PETase, which usually are unfavorable for PET recondensation and high-value derivative synthesis (Fig. 1b) .

In order to address these challenges, we reconstructed the ancestral sequence of PETaes (ASR-PETase) to understand the diversity of PET hydrolases in the process of evolution. Guided by the machine learning algorithm (ESM-1v model), we designed the ASR-PETase variants with great thermostability at the higher temperature (60 °C). Followed by constructing a two-enzyme system, we obtained the production of homogeneous TPA, which enables the closed-loop PET recycling by a tandem chemical and biological approach.

Design

BBa_K4484000 is aimed to achieve surface display of PETase and MHETase separately in Escherichia coli. However, the experimental results indicated that BBa_K4484000 showed low activity at 37 °C. By modifying the BBa_{_}K4484000, we hope to enhance its thermostability and catalytic efficiency. We started from reviving ancient PETase through ancestral sequence reconstruction (ASR-PETase), and used a series of databases (Swiss-prot, NCBI) and a maximum likelihood algorithm by Fireprot server to build the corresponding phylogenetic tree. After obtaining the ASR-PETases through the phylogenetic tree, we detected the activity of ASR-PETases and selected the promising ASR-PETase as protein scaffolds. We posited that highly focused protein engineering approaches cannot consider the evolutionary trade-off between overall stability and activity. To this end, we used a machine learning algorithm (ESM-1v model) to identify stabilizing variants. To demonstrate the driving force behind the increased degradation capacity at a higher temperature, we also investigated the MD simulation of ASR-PETase variants in the presence of PET.

Moreover, TPA produced by PETase often suffered from contamination by oligoethylene terephthalates, BHET, and MHET (BBa_{_}K4484000), which posed limitations on downstream applications. To address this problem, we constructed a two-enzyme system by combining ASR1-PETase variant and BHETase (BsEst), yielding the homogeneous TPA. To achieving the closed-loop PET recycling, we developed a tandem chemical-biological approach to leverage the advantages of chemical and biological depolymerization processes.

Result

Aim to improve the thermostability of the enzyme, we started from reconstructing the ancestor sequence of BBa_{_}K4484000, and obtained seven ancestor sequences (Fig. 2). Compared with the wild type PETase, the identity of ASR1 (21.37%) is significantly lower than that of ASR7 (67.25%) (Fig. 3). Similar degradation capacity and the lower sequence identity enable us to select the promising ASR1-PETase as protein scaffolds for protein engineering.

Next, we used a ESM-1v model to identify stabilizing variants, which is a machine learning method with unsupervised learning capability to successfully infer and predict unknown proteins by learning from known protein data (Fig. 4a). It can obtain the desired substitutions without any experimental data or additional training. The principle of ESM-1v is to measure the possibility of abrupt change by using the entropy value of the point. After ranking the probability of substitutions, the top 20 variants with the highest score (P177E, K249E, I223E, W120R, Q79N, S242G, L246H, Y46I, N81H, G201A, F213S, F275L, L122K, C287P, M17A, V265T, C26A, E125R, M40E) were selected for further experimental validation (Fig. 4b).

As showed in Fig. 5a, the ASR1-PETase variants maintained hydrolysis activity at 60 °C, whereas most of the wild type PETase activity was lost over 30 °C. This indicated that compared with BBa_{_}K4484000, the variants produced by machine learning had higher thermostability. The hydrolysis advantages of ASR1-V265T, ASR1-N81H, ASR1-W120R, and ASR1-L122K were presented on PET film at temperature of 30-60 °C. In details, ASR1-V265T, ASR1-N81H, ASR1-W120R, and ASR1-L122K exhibited increased BHET conversion, high up to 2.71-fold of ASR1-PETase. Meanwhile, the corresponding terminal product (TPA) of four variants was also maintained at high yields (2.58 to 2.88 mM) (Fig. 5b-d).

To address the challenge of mixed products by PETase in BBa_{_}K4484000, we combined those effective variants with BHETase (BsEst, identified in our previous study) to construct a two-enzyme system. According to the Fig. 6c, the results showed that the two-enzyme system developed in this program enables the production of homogeneous TPA. Importantly, the TPA yield of the two-enzyme system was much more than that of the single-enzyme system. Among them, the TPA production of ASR1-V265T/BsEst and ASR1-W120R/BsEst was increased by 1.7-fold and 1.8-fold over 120 h, respectively (Fig. 6b).

Through the analysis of the stability and activity of the four enzymes (PETase, ASR1-WT, ASR1-V265T, and ASR1-W120R), it indicated that the mechanism of the increased enzyme activity of the variants was that the number of water molecules in the substrate binding site increased, and the distance between the substrate and the active center was closer.

To demonstrate the driving force behind the increased degradation capacity of PETase (BBa_{_}K4484000), ASR1-WT, ASR1-V265T, and ASR1-W120R at a higher temperature, we investigated the MD simulation of ASR-PETase variants after the molecular docking with PET. RMSF value in Fig. 7a-b showed that ASR1-W120R has reduced flexibility compared to wild type PETase. Moreover, 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 wild type PETase, this suggested that the substrate binding site of the ASR1 variants could attract more water molecules (Fig. 7c). Additionally, the distance between the active site of the ASR1 variants and the substrate PET dimer core was less than that of PETase (Fig. 7d). This result suggested that compared with PETase, it was easier for the substrates to enter the active center of the ASR1 variants, therefore, promoting the transformation of the substrate. In conclusion, 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.

Reference

1. Jiang, S.; Su, T.; Wang, Z., Progress in Biodegradation of Polyethylene Terephthalate (PET). Plastics 2021, 50 (4), 90-95.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

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

12. 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.

13. 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.

14. 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.

15. 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.

16. 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.