Part:BBa_K5427077
Q-sensor
In designing our Q-sensor, we selected linkers from the study (Kolossov et al., 2008) due to their demonstrated ability to maintain precise spacing between donor and acceptor molecules, crucial for efficient energy transfer and fluorescence quenching. These linkers offer the necessary flexibility and controlled length, which we hoped would ensure the nanobody's optimal binding to Keratin (in our case) while minimizing signal interference. Their adaptability also allowed us to tailor the linkers to meet the specific structural needs of our system, improving both sensitivity and specificity in the quenching mechanism.
After conducting numerous interviews through human practises with professionals, we decided to cease the use of the Q-sensor in our project. Therefore, no charactization from wetlab was performed.
Sequence and Features
- 10COMPATIBLE WITH RFC[10]
- 12INCOMPATIBLE WITH RFC[12]Illegal NheI site found at 436
- 21COMPATIBLE WITH RFC[21]
- 23COMPATIBLE WITH RFC[23]
- 25COMPATIBLE WITH RFC[25]
- 1000INCOMPATIBLE WITH RFC[1000]Illegal SapI site found at 837
Constructs
Q-sensor (mScarlet-Linker 1-Chameleon-Linker 2-Nb-Linker 3-rShadow)
Protein Modeling
The models allowed us to test the folding of our FRET-compatible linkers (RL1, RL2, and RL3) for our Q-sensor and biosensor (Kolossov et al., 2008). Modeling also visualized and verified the interactions between the domains involved in the quenching system (mScarlet and ShadowR) for both our Q-sensor and biosensor. The linkers used had low pLDDT values in both the Q-sensor and biosensor, however, this was expected as AlphaFold2 is unable to fully model linkers. The regions where the linkers were did not have coverage as shown by the sequence coverage plots. Despite this, the linkers do show alpha helical structures and allow the quenching system to interact. The Ramachandran plots for the linkers generally show that the phi and psi angles are in acceptable ranges, which provides some validation for the generated structure of the linkers.
Figure 1 | Q-Sensor predicted local distance difference test (pLDDT) plot generated by AlphaFold2. Regions of low pLDDT represent the linkers where AlphaFold2 could not accurately model due to the lack of homologs.
Figure 2 | Q-Sensor multiple sequence alignment (MSA) sequence coverage plot generated by AlphaFold2. Regions of low coverage represent the linkers with a lack of homologs.
Figure 3 | Q-Sensor model generated by AlphaFold2 and visualized with Jmol. GIF was generated by FirstGlance.
Figure 4 | Q-sensor Ramachandran plot generated by SWISS-MODEL.
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