Part:BBa_K4122017

SP-GFP-His-Spytag+SP-RFP-cMyc-Snooptag+SP-CBM-SC-SC-SNC-SC-V5-7813

SP-GFP-His-Spytag+ SP-RFP-cMyc-Snooptag+ SP-CBM-SC-SC-SNC-SC-V5-7813

This part is composed of three parts:

Part 1:SP-GFP-His-Spytag BBa_K4122014

Part 2:SP-RFP-cMyc-Snooptag BBa_K4122015

Part 3:SP-CBM-SC-SC-SNC-SC-V5-7813 BBa_K4122016

Characterization-Introduction of Tag-Catcher system to co-display PETase and MHETase

This year, we replaced the promotor and terminator of GFP on the basis of last year. Promoter-FBA1 and Terminator-ADH2 would achieve better effect and orthogonality. And we used GFP and RFP as markers of successful construction of spycatcher and spytag syetem.

To attain co-display, we combined our display system with two selective protein binding systems, SpyTag-SpyCatcher and SnoopTag-SnoopCatcher. In our experiment, GFP and RFP were used to indicate the successful construction of Spycatcher/Spytag and Snoopcatcher/Snooptag systems. We initially tried two catcher types with a ratio of 1:3.

Fig.1 The construction of plasmid Ts-PGAPDH--TENO1A, the surface display system for displaying both GFP and RFP. (BBa_K4122017)

SC: Spycatcher BBa_K4122008; SNC: Snoopcatcher BBa_K4122010; V5: V5 tag BBa_K3829004; CBM: carbohydrate binding domain BBa_K4122006

GFP+ and RFP+ suggested the successful construction of Spycatcher/Spytag system and Snoopcatcher/Snooptag system.

Fig.2 The fluorescence result of the spy/snoop tag and catcher system.

A and D, bright field; B and E, Green fluorescence; C and F, Red fluorescence

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 BBa_K4122017 SequenceAndFeatures