Difference between revisions of "Part:BBa K4122016"

 
 
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<partinfo>BBa_K4122016 short</partinfo>
  
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<h3>Characterization-Introduction of Tag-Catcher system to co-display PETase and MHETase</h3>
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<P>To attain co-display, we combined our display system with two selective protein binding systems, SpyTag-SpyCatcher and SnoopTag-SnoopCatcher.
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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.</P>
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<img src="https://static.igem.wiki/teams/4122/wiki/parts/08-the-construction-of-plasmid-ts-pgapdh-teno1a-the-surface-display-system-for-displaying-both-gfp-and-rfp.png" style = "width:55%;">
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<br/><b>Fig.1</b> The construction of plasmid Ts-PGAPDH--TENO1A, the surface display system for displaying both GFP and RFP. (<a href="https://parts.igem.org/Part:BBa_K4122017">BBa_K4122017</a>)
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<p>SC: Spycatcher BBa_K4122008; SNC: Snoopcatcher BBa_K4122010; V5: V5 tag BBa_K3829004; CBM: carbohydrate binding domain BBa_K4122006</p>
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<p>GFP+ and RFP+ suggested the successful construction of Spycatcher/Spytag system and Snoopcatcher/Snooptag system.</p>
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<img src="https://static.igem.wiki/teams/4122/wiki/parts/09-the-fluorescence-result-of-the-spy-snoop-tag-and-catcher-system.png
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" style = "width:60%;">
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<br/><b>Fig.2</b> The fluorescence result of the spy/snoop tag and catcher system.
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<p>A and D, bright field; B and E, Green fluorescence; C and F, Red fluorescence</p>
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<h3>References</h3>
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<p>[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).</p >
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<p>[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).</p >
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<p>[3] Jianyi Yang, Yang Zhang. I-TASSER server: new development for protein structure and function predictions, Nucleic Acids Research, 43: W174-W181, 2015. </p >
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<p>[4] Lu, Hongyuan, et al. "Machine learning-aided engineering of hydrolases for PET depolymerization." Nature 604.7907 (2022): 662-667.</p >
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===Usage and Biology===
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<span class='h3bb'>Sequence and Features</span>
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<partinfo>BBa_K4122017 SequenceAndFeatures</partinfo>
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<!-- Uncomment this to enable Functional Parameter display

Latest revision as of 08:29, 6 October 2022

PGAPDH-SP-CBM-SC-SC-SNC-SC-V5-7813

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

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