Regulatory

Part:BBa_K4207035

Designed by: Jesper Mickos   Group: iGEM22_Aboa   (2022-09-30)


TCV toehold switch B1

1. Usage and Biology

Toehold switches are engineered riboregulators that control the expression of a downstream protein coding sequence. They can be designed to detect virtually any sequence. Toehold switches are designed in silico so that they fold into a pre-determined secondary structure. This structure contains a stable stem-loop that sequesters the ribosome binding site (RBS) and the start codon, thus preventing translation. After a specific trigger RNA binds to the binding site of the toehold, the lower part of the stem-loop unfolds, revealing the start codon. A weak stem remains, but this structure unfolds upon ribosome binding to the RBS, starting translation (Green et al., 2017). This toehold switch was designed to detect conserved sequences in the Tomato chlorosis virus genome. The structural change of the toehold switch is illustrated in Figure 1.

Figure 1. Toehold switch mechanism. This animation illustrates the operation of the toehold switch. Initially, the structure is in an inactive state and the RBS and the start codon are hidden in the stem-loop. When a specific trigger binds to the binding site, the stem-loop structure opens and the ribosome binding site and start codon are revealed.

To use this toehold switch, it should be assembled in a construct containing a promoter, the toehold switch, a protein-coding sequence, and optionally a terminator if the sensor is not to be used as linear. To prevent frame-shifting, the toehold switch should be combined with the coding sequence using scarless assembly.

Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    COMPATIBLE WITH RFC[21]
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]

2. Design

This toehold switch was designed according to the B-series ideal structure from Pardee et al. (2016). This structure was improved from the original toehold switch structure (Green et al., 2014) to reduce translational leakage. We screened the Tomato chlorosis virus genome for conserved sequences. Each sequence was divided into 36-nucleotide long subsequences and we designed toehold switches designed to specifically bind to the sequence. This toehold switch was designed using the 21-nucleotide linker (Green et al., 2014). We assigned a score for each toehold switch based on the three-parameter fit from Ma et al. (2018) and selected the best-ranking toehold switches for our library.

3. Characterization

Score predicted by our model: 21,72

  • Figure 2. Structure of inactive form. The picture was created with the NUPACK web app (Zadeh et al., 2010)

  • Figure 3. Structure of active form. The picture was created with the NUPACK web app (Zadeh et al., 2011)

The expected structures for inactive and active toehold switches were modeled by using NUPACK’s analysis tool (Zadeh et al., 2011). This toehold switch’s inactive structure does not fully correspond to the ideal structure. Therefore, the ability to exhibit trigger-dependent translation is uncertain. Due to the aforementioned factors, the toehold switch may not be as functional as desired.

4. Conclusion

Based on the data provided by our model as well as the visualization of the structures, conclusions about this toehold switches functionality can not be drawn. This toehold switch could be functional in a sensor construct, but more promising candidates could be designed with more optimized design algorithms. Verifying the functionality is necessary to create a viable toehold sensor construct.

5. References

Green, A. A., Kim, J., Ma, D., Silver, P. A., Collins, J. J., & Yin, P. (2017). Complex cellular logic computation using ribocomputing devices. Nature, 548(7665), 117–121. https://doi.org/10.1038/nature23271

Green, A., Silver, P., Collins, J., & Yin, P. (2014). Toehold Switches: De-Novo-Designed Regulators of Gene Expression. Cell, 159(4), 925–939. https://doi.org/10.1016/j.cell.2014.10.002

Fornace, M. E., Porubsky, N. J., & Pierce, N. A. (2020). A Unified Dynamic Programming Framework for the Analysis of Interacting Nucleic Acid Strands: Enhanced Models, Scalability, and Speed. ACS Synthetic Biology, 9(10), 2665–2678. https://doi.org/10.1021/acssynbio.9b00523

Ma, D., Shen, L., Wu, K., Diehnelt, C. W., & Green, A. A. (2018). Low-cost detection of norovirus using paper-based cell-free systems and synbody-based viral enrichment. Synthetic Biology, 3(1). https://doi.org/10.1093/synbio/ysy018

Pardee, K., Green, A. A., Takahashi, M. K., Braff, D., Lambert, G., Lee, J. W., Ferrante, T., Ma, D., Donghia, N., Fan, M., Daringer, N. M., Bosch, I., Dudley, D. M., O’Connor, D. H., Gehrke, L., & Collins, J. J. (2016). Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components. Cell, 165(5), 1255–1266. https://doi.org/10.1016/j.cell.2016.04.059

Zadeh, J. N., Steenberg, C. D., Bois, J. S., Wolfe, B. R., Pierce, M. B., Khan, A. R., Dirks, R. M., & Pierce, N. A. (2010, November 17). NUPACK: Analysis and design of nucleic acid systems. Journal of Computational Chemistry, 32(1), 170–173. https://doi.org/10.1002/jcc.21596


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