Part:BBa_K4122027
SP-PETase-His-Spycatcher-T+SP-MHETase-cMyc-Snoopcatcher-T + SP- V5-CBM -ST-ST-SNT-781
This part is compose of 3 parts:
P-SP-PETase-His-Spycatcher-T
+ SP-MHETase-cMyc-Snoopcatcher-T
+ SP-CBM-V5-ST-ST-SNT-7813 (ST:SNT=2:1)
Optimize PETase and MHETase surface display Ratios-Final optimized system
We started with a ratio of snooptag: spytag=1:3. In order to obtain better catalytic effect, we optimized its proportion and successfully constructed scaffolds with different proportions.
Fig.1 Successful construction of different scaffolds ratios. (
We tested the effect of different ratios by HPLC, and found that the ratio of 2:1 performed the best among all the groups.
We started with a ratio of snooptag: spytag=1:3. In order to obtain better catalytic effect, we optimized its proportion and successfully constructed scaffolds with different proportions.
Fig.2 HPLC analysis of degraded PET with displaying PETases and MHETase (different ratios).
The results of degraded PET film further confirmed the results.
Fig.3 Degradation of PET film with the strainS-F:M=2:1.
References
[1] Wei Zheng, Chengxin Zhang, Yang Li, Robin Pearce, Eric W. Bell, Yang Zhang. Folding non-homology proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Reports Methods, 1: 100014 (2021).
[2] Chengxin Zhang, Peter L. Freddolino, and Yang Zhang. COFACTOR: improved protein function prediction by combining structure, sequence and protein-protein interaction information. Nucleic Acids Research, 45: W291-299 (2017).
[3] Jianyi Yang, Yang Zhang. I-TASSER server: new development for protein structure and function predictions, Nucleic Acids Research, 43: W174-W181, 2015.
[4] Lu, Hongyuan, et al. "Machine learning-aided engineering of hydrolases for PET depolymerization." Nature 604.7907 (2022): 662-667.
Sequence and Features| None |