Coding

Part:BBa_K4803016

Designed by: Kotaro MURAI   Group: iGEM23_UTokyo   (2023-10-10)

TVMV^Thr-AI_Disorder

This biobrick is a Part of TVMVThr-AI [1] that improves the stability of Thr-CS by mutating the linker before and after Thr-CS.

Usage and Biology

This Part is a linker-connected part of TVMV Protease, Thr-Cleavage Site, and Inhibitor of TVMV Protease. In the presence of Threonine Protease, the linker between TVMV Protease and Inhibitor is cleaved and TVMV Protease is activated. By preparing this Part in the cell in advance, TVMV Protease can be activated above the expression level of Threonine Protease, which is expected to have an amplification effect when viewed from the perspective of the entire system.

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Figure1: The Mechanism of Amplification System

In addition, the absolute activation of TVMV protease is expected to be high because this Part uses the sequence with the lowest stability of the cleavage site, which we evaluated the stability by changing the linkers before and after the Thr-Cleavage Site in various ways in the Dry simulation. The absolute activation of TVMV protease is expected to be high.

Characterization

In UTokyo2023 project, we modified the linkers around the Inhibitor domain and cleavage site in about 20 different patterns based on the sequences used in the paper, and compared the prediction accuracy with respect to the original sequences and symmetric sites. Local Colab Fold 1.5.2 [2] was used for protein structure prediction and pLDDT values were used for comparison of prediction accuracy. In addition, the predicted 3D structures were compared to ensure that the molecule made from the mutated sequence would have the same function as before the mutation.

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(b)
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Figure 2: (a) Comparison graphs of pLDDT at each base of TVMVThr-AI and mutated TVMVThr-AI sequences. (b) Comparison of 3D structures predicted by Local Colab Fold.

Reference

[1] Stein, V., & Alexandrov, K. (2014). Protease-based synthetic sensing and signal amplification. Proceedings of the National Academy of Sciences, 111(45), 15934–15939. https://doi.org/10.1073/pnas.1405220111

[2] Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2022). Colabfold: Making protein folding accessible to all. Nature Methods, 19(6), 679–682. https://doi.org/10.1038/s41592-022-01488-1


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