Difference between revisions of "Part:BBa K5236025"
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<partinfo>BBa_K5236025 short</partinfo>
 
<partinfo>BBa_K5236025 short</partinfo>
  
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The sequence of BhrPETase was identified by the Shingo group in a metagenomic study on uncultured thermophiles, and was deposited into the NCBI database by the group in 2018 and annotated as a PET hydrolase [1]. This basic part encoding the BhrPETase, which has been predicted and optimized by Wu et al. And was constructed and modified as WT BhrPETase in our project.The superior activity and thermostability of BhrPETase rendered it one of the most promising PETases for plastic waste recycling and bioremediation applications in the future [2].
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The sequence of BhrPETase was identified by the Shingo group in a metagenomic study on uncultured thermophiles, and was deposited into the NCBI database by the group in 2018 and annotated as a PET hydrolase [1]. This basic part encoding the BhrPETase, which has been predicted and optimized by Wu et al. Si-face binding is the main binding pose of PET in the active site of BhrPETase. And was constructed and modified as WT BhrPETase in our project.[2] The superior activity and thermostability of BhrPETase rendered it one of the most promising PETases for plastic waste recycling and bioremediation applications in the future [3].
 
<center><html><img src ="https://static.igem.wiki/teams/5236/part-images/3d-structure-of-bhrpetase.png" width = "50%"><br></html></center>
 
<center><html><img src ="https://static.igem.wiki/teams/5236/part-images/3d-structure-of-bhrpetase.png" width = "50%"><br></html></center>
 
<center>Fig.1 The 3D protein structure of WT BhrPETase </center>
 
<center>Fig.1 The 3D protein structure of WT BhrPETase </center>
  
 
===Usage and Biology===
 
===Usage and Biology===
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We trained a Transformer model on 1007 homologous PETase protein sequences obtained from the UniProt Database using the masked language model (MLM) training method. This approach allows the model to learn contextual information about amino acid sequences and predict masked residues accurately [3]. The BhrPETase mutants that scored in the top four in the trained model were used in the construction and tested.
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We trained a Transformer model on 1007 homologous PETase protein sequences obtained from the UniProt Database using the masked language model (MLM) training method. This approach allows the model to learn contextual information about amino acid sequences and predict masked residues accurately [4]. The BhrPETase mutants that scored in the top four in the trained model were used in the construction and tested.
  
 
<center><html><img src ="https://static.igem.wiki/teams/5236/model-pics/2761727664593-pic.jpg" width = "50%"><br></html></center>
 
<center><html><img src ="https://static.igem.wiki/teams/5236/model-pics/2761727664593-pic.jpg" width = "50%"><br></html></center>
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[1] Kato, Shingo, et al. “Long-Term Cultivation and Metagenomics Reveal Ecophysiology of Previously Uncultivated Thermophiles Involved in Biogeochemical Nitrogen Cycle.” Microbes and Environments, vol. 33, no. 1, Jan. 2018, pp. 107–10. https://doi.org/10.1264/jsme2.me17165.
 
[1] Kato, Shingo, et al. “Long-Term Cultivation and Metagenomics Reveal Ecophysiology of Previously Uncultivated Thermophiles Involved in Biogeochemical Nitrogen Cycle.” Microbes and Environments, vol. 33, no. 1, Jan. 2018, pp. 107–10. https://doi.org/10.1264/jsme2.me17165.
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[2]Xi, X., Ni, K., Hao, H., Shang, Y., Zhao, B., & Qian, Z. (2020). Secretory expression in Bacillus subtilis and biochemical characterization of a highly thermostable polyethylene terephthalate hydrolase from bacterium HR29. Enzyme and Microbial Technology, 143, 109715. https://doi.org/10.1016/j.enzmictec.2020.109715
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[2]Wang, N., Li, Y., Zheng, M., Dong, W., Zhang, Q., & Wang, W. (2024b). BhrPETase catalyzed polyethylene terephthalate depolymerization: A quantum mechanics/molecular mechanics approach. Journal of Hazardous Materials, 477, 135414. https://doi.org/10.1016/j.jhazmat.2024.135414
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[3] Lu, Hongyuan, et al. “Machine Learning-aided Engineering of Hydrolases for PET Depolymerization.” Nature, vol. 604, no. 7907, Apr. 2022, pp. 662–67. https://doi.org/10.1038/s41586-022-04599-z.
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[3]Xi, X., Ni, K., Hao, H., Shang, Y., Zhao, B., & Qian, Z. (2020). Secretory expression in Bacillus subtilis and biochemical characterization of a highly thermostable polyethylene terephthalate hydrolase from bacterium HR29. Enzyme and Microbial Technology, 143, 109715. https://doi.org/10.1016/j.enzmictec.2020.109715
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[4] Lu, Hongyuan, et al. “Machine Learning-aided Engineering of Hydrolases for PET Depolymerization.” Nature, vol. 604, no. 7907, Apr. 2022, pp. 662–67. https://doi.org/10.1038/s41586-022-04599-z.

Revision as of 02:59, 2 October 2024


BhrPETase

The sequence of BhrPETase was identified by the Shingo group in a metagenomic study on uncultured thermophiles, and was deposited into the NCBI database by the group in 2018 and annotated as a PET hydrolase [1]. This basic part encoding the BhrPETase, which has been predicted and optimized by Wu et al. Si-face binding is the main binding pose of PET in the active site of BhrPETase. And was constructed and modified as WT BhrPETase in our project.[2] The superior activity and thermostability of BhrPETase rendered it one of the most promising PETases for plastic waste recycling and bioremediation applications in the future [3].


Fig.1 The 3D protein structure of WT BhrPETase

Usage and Biology

We trained a Transformer model on 1007 homologous PETase protein sequences obtained from the UniProt Database using the masked language model (MLM) training method. This approach allows the model to learn contextual information about amino acid sequences and predict masked residues accurately [4]. The BhrPETase mutants that scored in the top four in the trained model were used in the construction and tested.


Fig.2 The overall pipeline of our model training method.


To construct plasmids, we’ve designed primers and performed PCRs. Then, our genes were recombined into plasmids and transformed into chassis. By conducting colony PCR, we are able to test if our parts have been transformed into E.coli successfully. The following result of electrophoresis proves that we’ve inserted genes into chassis since the sequence containing our mutated genes has a total of 798 base pairs and the results are in the right location.


Fig.3 The DNA gel electrophoresis result

In order to secret WT BhrPETase, we start to construct plasmid for protein expression. As figure.4 shows, we insert several parts to accomplish the expression for WT BhrPETase.


Fig.4 The composition of BhrPETase WT

The function of each parts:

T7 promoter: A Strong promoter recognized by T7 RNA polymerase, used to regulate gene expression of recombinant proteins.

Lac operator: Operator that can be activated by IPTG, used to control gene expression by lactose or IPTG.

RBS: Ribosome binding site.

WT BhrPETase:The basic part encoding the BhrPETase who had been mutated.

pelB: The sequence encodes a signal peptide that enables secretory expression of PETase.

6xHis: A label for protein purification

T7 terminator: Terminates transcription.


Fig.5 The contructed plasmid with WT BhrPETase


When we had completed the plasmid construction and transformation. We need to construct and test the BhrPETase activity. .



Fig.6 Mutated BhrPETase Dynamic Curve

We successfully submitted BhrPETase as a standardized part and experiments showed that BhrPETase was shown to have higher enzymatic activity than IsPETase at room temperature. After our sequence optimization, the appearance of BhrPETase-N205G is expected to improve the activity of BhrPETase.


Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    COMPATIBLE WITH RFC[21]
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]


Reference

[1] Kato, Shingo, et al. “Long-Term Cultivation and Metagenomics Reveal Ecophysiology of Previously Uncultivated Thermophiles Involved in Biogeochemical Nitrogen Cycle.” Microbes and Environments, vol. 33, no. 1, Jan. 2018, pp. 107–10. https://doi.org/10.1264/jsme2.me17165. [2]Wang, N., Li, Y., Zheng, M., Dong, W., Zhang, Q., & Wang, W. (2024b). BhrPETase catalyzed polyethylene terephthalate depolymerization: A quantum mechanics/molecular mechanics approach. Journal of Hazardous Materials, 477, 135414. https://doi.org/10.1016/j.jhazmat.2024.135414 [3]Xi, X., Ni, K., Hao, H., Shang, Y., Zhao, B., & Qian, Z. (2020). Secretory expression in Bacillus subtilis and biochemical characterization of a highly thermostable polyethylene terephthalate hydrolase from bacterium HR29. Enzyme and Microbial Technology, 143, 109715. https://doi.org/10.1016/j.enzmictec.2020.109715 [4] Lu, Hongyuan, et al. “Machine Learning-aided Engineering of Hydrolases for PET Depolymerization.” Nature, vol. 604, no. 7907, Apr. 2022, pp. 662–67. https://doi.org/10.1038/s41586-022-04599-z.