Part:BBa_K4122022
P-SP-PETase-His-Spycatcher-T
P-SP-PETase-His-Spycatcher-T
Promotor-FBA1
Teminator-ADH2
This part is one of the components of BBa_K4122025.
Different from BBa_K4122020 that did not show enzyme activity, we moved V5 tag before the spytag to fold correctly. Besides, we exchanged the position of tags and catchers.
Part One:Moved V5 tag forward
When replacing RFP and GFP with MHETase and PETase, we did not observe immunofluorescence with secondary antibodies that should theoretically bind specifically to the V5 tag.
To analyze whether PETase-spytag and MHETase-snooptag fused protein folded correctly, we constructed a model of the fusion protein, we used prediction software such as trRosetta and ITASSER to construct the structure. The evaluation results of the two models shows the structure is convincing. So, no enzyme activity could be a steric hindrance between the fusion protein and the scaffold (See Modeling for details, model link here).
Similarly, we used I-TASSER to model our “CBM-SC-SC-SNC-SC-V5-7813” scaffold. When the display system is constructed, immunofluorescence cannot be detected, presumably as the V5 tag has been obstructed. To verify the theory, we predicted the model of the overall protein using the I-TASSER server and discovered that the V5 tag is truly embedded by other proteins.
It can be seen from the figure that the red component (V5 tag) is blocked by other components, meaning the V5 tag cannot function ideally as designed. We presumed the V5 tag would be available if it was located at the sequence's beginning, as the catchers may have a larger size that blocks the V5 tag if it is located at the end of the sequence.
Fig.1 Model of scaffold CBM-SC-SC-SNC-SC-V5-7813 predicted by I-TASSER server.
To make V5 tag and in turn immunofluorescence visible, we changed V5 tag’s position to the front of the plasmid. This edition means V5 tag transcription takes place before catchers’ transcription, lowering the possibility that large seized catcher protein obstructing V5-tag. After altering the V5 tag’s location, we predicted the model again using I-TASSER to ensure its feasibility.
Fig.2 Model of scaffold CBM-V5-SC-SC-SNC-SC-7813 predicted by I-TASSER server.
Finally, the results of changing the position of V5 tag were proved to be effective and then we continued to put V5 tag in the front of tags.
Fig.3 Successful construction of changing the position of V5-tag.
Part Two:Optimize the scaffold by exchanging tag and catcher to reduce the molecular mass of the proteins.
We exchanged the position of the tags and catchers. In addition, we also changed V5 tag's position to prevent it being covered by the protein. We built the scaffolds with Spy tag and Snoop tag that bind to Spy catcher and Snoop catcher to display the two enzymes. The construction changed from "SP-CBM-SC-SC-SNC-SC-V5-7813" to "SP-CBM-V5-ST-ST-SNT-ST-7813". Our predicted model revealed it was feasible and actual wet experiment proved its viability.
Fig.4 The illustration of the optimized scaffold SP-CBM -V5-ST-ST-SNT-ST - 7813. (BBa_K4122025)
Fig.5 The illustration of the optimized scaffold SP-CBM -V5-ST-ST-SNT-ST-7813. (BBa_K4122025)
By observing with fluorescence microscope, we successfully detect the immunofluorescence (FITC-Fluorescein isothiocyanate isomer) outside the yeast showing the functionality of our optimized system.
Fig.6 FITC immunofluorescence of optimized scaffolds under the fluorescent microscopes.
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 |