Difference between revisions of "Part:BBa K5249029"

Latest revision as of 13:59, 2 October 2024

BEST NEW BASIC PART

IsPETase^PA:THR-122-PRO

Overview

In 2016, researchers discovered a bacterial strain, *Ideonella sakaiensis*, which can efficiently degrade PET. They also reported a hydrolase secreted by this strain, PETase, which can hydrolyze PET into bis(2-hydroxyethyl) terephthalate (BHET), mono(2-hydroxyethyl) terephthalate (MHET), and terephthalic acid (TPA). Under conditions of ambient temperature at 30°C and pH 7.0, IsPETase exhibited activities on PET film and BHET that were 120.0, 5.5, and 88.0 times higher compared to previously identified enzymes TfH, LCC, and FsC, respectively. Some studies have also rationalized the design of PETase to enhance degradation efficiency, and site-saturation mutagenesis was performed on key sites of IsPETase, resulting in the S92P/D157A (IsPETasePA) variant, which exhibited a 24.75-fold increase in activity compared to the wild type at 40°C.

Consequently, our project SUPERB utilized machine learning to predict potential efficient mutant types and the glycosylation mutation sites based on the known IsPETase^PA, aiming to enhance the efficiency of plastic degradation under milder and acidic conditions while exploiting Pichia pastoris to improve enzyme expression. Our project was introduced as follows:

Rational enzyme design by machine learning and molecular docking

To improve the stability of plastic-degrading enzymes, we want to use machine learning to predict mutations of enzymes. By reviewing the literature, we adopt a three-dimensional (3D) self-supervised convolutional neural network (CNN), MutCompute (https://mutcompute.com), to identify potential mutations.

Figure 1. IsPETase^PA mutation sites map

A comprehensive virtual mutation scan was then performed on the modified enzyme (Figure 1). The predicted distribution is mapped onto the protein crystal structure to identify locations where wild-type amino acid residues are less adaptable and could be replaced by more suitable substitutes, and the predictions are then ranked according to the predicted probability (a multiple change in fitness).

Meanwhile, through YinOYang 1.2-DTU Health Tech-Bioinformatic Services and NetNGlyc 1.0-DTU Health Tech-Bioinformatic Services*, we input amino acid sequences on these two sites to predict O-glycosylation and N-glycosylation sites, respectively. After identifying the glycosylation sites, we selected the top 10 or 15 points based on the machine learning results.

Finally, four mutation sites (S29A, T59S, T122P, N183A) were screened on IsPETase^PA, and their ability to bind to polyethylene terephthalate (PET) and 1-(2-hydroxyethyl)4-methyl terephthalate (HEMT) was verified through molecular docking.

Figure 2. Molecular docking. a. IsPETase^PA docking with PET; b. Results of IsPETasePA docking with PET; c. IsPETase^PA docking with HEMT; d. Results of IsPETase^PA docking with HEMT

According to the docking results (Figure 2), combining the binding free energy, root mean square deviation (RMSD), hydrogen bond number and length from Autodock, we found that the 122nd and 183rd sites of IsPETase^PA from ranked top 10 were the optimal mutation candidates to improve the enzyme activity.

The screening of chassis cells and expression of enzymes

a. Protein expression in BL21

To evaluate different chassis cells, *Escherichia coli* BL21 (DE3) and *Pichia pastoris* GS115 were selected for enhanced protein expression. The gene encoding EIsPETase (BBa_K5249012 ), fused with mCherry and a 6xHis tag at the C-terminal, was synthesized and subsequently cloned into the pET28a plasmid for enzyme expression under IPTG induction. mCherry acts as a red fluorescent protein, serving as a visual label for genes and cells of interest. By assessing the coloration of the strains, we can quantitatively evaluate the level of enzyme expression.

Figure 3. a. Recombinant plasmid map of T7-IsPETase; b. The verification of EIsPETase by PCR method; c. E. coli expressing EIsPETase fused with red fluorescence protein; d. The cell pellet showing purple color of EIsPETase

Notably, when the EIsPETase enzyme was expressed in vivo, the color of the BL21 strain transitioned from white to pink (Figure 3e). Then we can used His-tag affinity purification method to obtain purified EIsPETase.

b. Protein expression in *Pichia pastoris*

The IsPETase^PA (BBa_K5249002)was inserted into the pHKA plasmid under the control of the AOXm promoter with the MF signal peptide. Moreover, all mutant enzyme related plasmids were constructed based on the IsPETase^PA plasmid, and they were obtained by PCR using primers containing the mutation sites, generating SER-29-ALA (BBa_K5249027), THR-59-SER (BBa_K5249028) and ASN- 183-ALA (BBa_K5249030). After recombinant plasmids were introduced into GS115 via electroporation, the expression of enzyme can be achieved by activating AOXm promoter using methanol.

Figure 4. a. Recombinant plasmid map of AmM-IsPETase^PA; b. The verification of IsPETase^PA linearization; c. Protein concentration of IsPETase^PA; d. SDS-PAGE of IsPETase^PA

Through the determination of protein concentration (Figure 4), the protein level reached the highest concentration on the fourth day, and the yield could reach 8.0 mg/mL. As a result, IsPETase^PA is able to achieve high levels of protein expression in GS115. At the same time, we found that the protein of IsPETase^PA can produce multiple bands, which is caused by the glycosylation of the protein. Moreover, it was reported that IsPETase^PA is a 3-polymer which may increase the number of protein bands.

Figure 5. a.b.c.d: recombinant plasmid map of mutant IsPETase^PA e.f.g.h: the verification of four mutant IsPETase^PA from PCR, e. Protein concentration of SER-29-ALA; f. Protein concentration of THR-59-SER; g. Protein concentration of THR-122-PRO; h. Protein concentration of ASN-183-ALA i:M: DL10000 marker, 1. SER-29-ALA; 2. THR-59-SER; 3.THR-122-PRO; 4. ASN- 183-ALA Through the determination of protein concentration (Figure 7), all mutant enzymes were expressed well in GS115, producing protein 7-8 mg/mL. Different mutant enzymes showed different protein concentration at different pH, showing different stability of protein under acidic conditions. ASN-183-ALA showed lowest assistance to acidic environment.

3. Functional verification of enzymes

3.1 Depolymerization of the PET Film by rude enzyme and HPLC analysis

The EIsPETase was purified for plastic degradation and the IsPETase^PA and mutant variant THR-122-PRO functioned in form of rude enzyme. 250 μg/mL of purified or rude enzyme was added into the 96 well microplate which contained PET film (diameter of 4.5 mm and weight of 2.5 mg, Goodfellow). The plastic degradation products were detected by HPLC with TPA and MHET as the standards.

Figure 6. The plastic degradation by IsPETase^PA under different conditions at 24 h. a. pH 9 with 30℃, 40℃ or 50℃; b. 30 ℃ with pH 6.0 or 7.0.

Figure 7. Plastic degradation in 96-well plates

It was found that the enzyme IsPETase^PA expressed by GS115 was better than EIsPETase expressed by BL21 when pH was 9, no matter at 30,40 or 50℃ (Figure 8 and 9). In particular, at 40℃, the total amount of IsPETase^PA degradation products (TPA+MHET) increased by 130.7% compared to EIsPETase，reaching 0.3 mM. Using machine learning and molecular docking, we attempted to find better enzymes that could efficiently degrade plastics under Pichia pastoris fermentation conditions (30 ° C, pH 6.0 or 7.0) and achieved the biotransformation of degradation products into high-value Bacterial cellulose. Therefore, in combination with deglycosylation engineering, we firstly selected the amino acid site of THR122 for mutation, mutated it into PRO, and generated the mutant enzyme THR-122-PRO. Interestingly, under the condition of 30℃ and pH 6.0, the enzyme activity of THR-122-PRO increased by 475.1%, which means that the ability of THR-122-PRO to degrade plastics under acidic conditions was significantly up-regulated.

Figure 8. The plastic degradation by IsPETase^PA under different conditions at 24 h. a. 30℃ with pH 6.0, 7.0, 8.0, 9.0 and 10.0; b. 40℃ with pH 6.0, 7.0, 8.0, 9.0 and 10.0

We searched for other mutation sites and mutated three other enzymes. Under the condition of Pichia pastoris fermentation (30℃, pH 6.0 or 7.0), THR-122-PRO was the most prominent. Through our previous experiments and literature, it was found that the optimal enzyme activity condition of IsPETasePA was 40℃ and pH 9.0. Under optimal enzyme activity conditions, THR-122-PRO still performed best compared with other mutant proteins. Therefore, THR-122-PRO is our BEST NEW BASIC PART.

3.2 Depolymerization of the PET Film by rude enzyme and scanning electron microscopy (SEM) analysis

250 μg/mL of THR-122-PRO and IsPETasePA rude enzyme were tested using 96 well microplate. The cultivation lasted for 120 h without changing fresh enzyme at 40℃ and pH 9.0. SEM analysis of the PET films treated by enzyme.

Figure 8. Morphological changes of plastics before and after degradation. a. PET film after incubation with IsPETasePA; b PET film after incubation with THR-122-PRO.

. Conclusions In summary, through machine learning, deglycosylation mutation and molecular docking test, THR-122-PRO is our BEST NEW BASIC PART (BBa_K5249029). Through our previous experiments and literature, it was found that the optimal enzyme activity condition of IsPETasePA was 40℃ and pH 9.0. Under optimal enzyme activity conditions, THR-122-PRO showed the best performance compared with other mutant proteins. Under the condition of 30℃ and pH 6.0, the enzyme activity of THR-122-PRO was increased by 431.1%, which means that the ability of THR-122-PRO to degrade plastics under acidic conditions was significantly up-regulated, which make it possible to biotransform degradation products under such culture conditions to produce high-value Bacterial cellulose. Finally, the machine-learning and molecular docking we used will be an effective strategy for research work on plastic-degrading enzyme mutation. It provides an innovative direction for future modifications in terms of iGEM and plastic-degrading enzymes, and we hoped that the accuracy of the machine-learning algorithm can be continuously improved and upgraded in the future, which will in turn contribute to synthetic biology.

References 1. Wei Yi, Xiao Yunjie, Yang Haitao, Wang Zefang. Polyethylene terephthalate hydrolase IsPETase and its application prospect. Acta Microbiologica Sinica, 2023, 63(1): 15-29. 2. GAO Y, ZHENG Y, QI Z, et al. Enhancing the biodegradation of bis(2‐hydroxyethyl) terephthalate by an IsPETasePA and MHETase dual‐enzyme system [J]. Journal of Chemical Technology & Biotechnology, 2024, 99(8): 1860-70.