Cycle1: Construct the Ancestral Sequence of PETase
Design
In 2016, Yoshida et al. from Kyoto Institute of Technology identified a IsPETase 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 IsPETase 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).
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.
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.
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.
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.
Build
Following the computations using ESM-1v, 20 potential variants with the highest scores were selected for experimental verification (Fig. 6).
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.
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).
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 |
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.
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.
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.
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.
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.
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.
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.
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.