Part:BBa_K4165030
HtrA1 switch 10
This composite part consists of T7 promoter (BBa_K3633015), lac operator (BBa_K4165062), pGS-21a RBS (BBa_K4165016), 6x His-tag (BBa_K4165020), H1A peptide (BBa_K4165000), GGGGSG (BBa_K4165017), TD28rev (BBa_K4165006), GGSGGGG (BBa_K4165018), WWW (BBa_K4165007), SPINK8 inhibitor (BBa_K4165010) and T7 terminator (BBa_K731721).
Usage and Biology
Switch 10 is used to mediate the activity of HTRA1. It is composed of 3 parts connected by different linkers; an HtrA1 PDZ peptide, a clamp of two targeting peptides for tau or amyloid beta, and a catalytic domain inhibitor. Activating HTRA1 requires a conformational change in the linker, eliminating the attached inhibitor from the active site. The conformational rearrangement can be mediated through the binding of affinity clamp to tau or beta-amyloid. This binding will result in a tension that detaches the inhibitor from the active site.
The TD28REV and WWW peptides which are considered as tau binding peptides are proved experimentally to bind with tau inhibit the aggregations of tau aggregations respectively. The H1A peptide was also proven to bind with the PDZ of HtrA1 experimentally. The last part which is the inhibitor which is mainly a serine protease inhibitor, and since our protease is a serine protease, so it will act and inhibit the Protein. The whole construction was similarly proved from literature.
Sequence and Features
- 10COMPATIBLE WITH RFC[10]
- 12COMPATIBLE WITH RFC[12]
- 21COMPATIBLE WITH RFC[21]
- 23COMPATIBLE WITH RFC[23]
- 25COMPATIBLE WITH RFC[25]
- 1000COMPATIBLE WITH RFC[1000]
Dry Lab
The top model is TrRoseeta model and the workflow to reach the model will be described below.
Figure 1. The 3D structure of switch 10 protein Visualized by Pymol. Red: Tau binding peptides,
blue: H1A peptide, cyan: inhibitor, and green: linkers.
The pipeline for creating this model is discussed in detail in the section below:
Switch construction Pipeline
Figure 2. A figure which describes our Dry-Lab Modelling Pipeline. By team CU_Egypt 2022.
1) Modelling
Since our Switch parts (HTRA1 binding peptide, TAU, and Beta-amyloid Binding peptide) do not have experimentally acquired structures, we modeled each one of them separately. This approach is done using both denovo modeling (ab initio) and template-based modeling. For modeling small peptides of our system, we used AppTest and Alphafold.
2) Structure Assessment
In order to assess the quality of generated structures, we used the Swiss-Model tool, which gives an overall quality of any 3D structure (For more information, please check our Modeling page.
3) Quality Assessment
Using the code created by us (CU_Egypt 2022), we use the JSON files created from the structure assessment step in Swiss-Model to rank all the models out of score 6. For more information: Programming club page code under the name of Modric..
4) Filtering
We take the top-ranked models from the previous steps that have either a score of 5 or 6
5) Docking
The top models of inhibitor and HTRA Binding Peptide are docked with HtrA1, and the top models of the clamps are docked with the Target protein, that is, in our case is Beta-amyloid (BBa_K4165004).
switch 10 vs HtrA1 trimer:
ΔG = -20.041
Figure 3. The 3D structure of switch 10 docked to HtrA1 Visualized by Pymol
6) Ranking
The docking results are ranked according to their PRODIGY results. For more information: (Link Docking page).
7) Top Models
The results that came out from PRODIGY are ranked and the top models are chosen to proceed with to the next step. For more information: (Link Docking page).
8) Alignment
Docked structures are aligned. This means that the HtrA1- binding peptide complex is aligned with the second complex which is the HtrA1-inhibitor complex to check whether they bound to the same site or not.
Figure 5. Aligned structures of HtrA1 binding peptide 1 docked to HtrA1 and inhibitor docked to HtrA1. </p>
9) Linker length
The linker lengths are acquired by seeing the distance between the inhibitor and the HtrA1 binding peptide, which is between both C terminals, N terminals, C- and N- terminal, and N- and C-terminals.
| CN | CC | NC | NN |
|---|---|---|---|
| 6.073 | 6.476 | 6.555 | 6.869 |
Table 1: The average linker amino acid length of all possibilities.
10) Assembly
After settling on the linkers lengths, now we will proceed to the assembly step of the whole system, which is done using TRrosetta, AlphaFold, RosettaFold, and Modeller.
11) Structure Assessment
In order to assess the quality of our structures, we used the Swiss-Model tool, which gives an overall quality of any 3D structure (For more information, please check our Modeling page.
12) Quality Assessment
Using the code created by us (CU_Egypt 2022), we use the JSON files created from the structure assessment step in Swiss-Model to rank all the models For more information, please proceed to our Programming club under the name of Modric.
| cbeta_deviations | clashscore | molprobity | ramachandran_favored | ramachandran_outliers | Qmean_4 | Qmean_6 |
|---|---|---|---|---|---|---|
| 0 | 0 | 1.49 | 100 | 0 | 2.023586 | 1.78381 |
Table 2: Quality assessment parameters of Switch 10 model.
13) Alignment
The docked structures are then aligned and compared to the basic parts, which are docked with the protein of interest (HtrA1). The structures with the least RMSD are chosen following the recommended range provided by CAPRI protocol.
| RMSD Before Docking | RMSD After Docking |
|---|---|
| 1.739 | 1.894 |
Conclusion
The top model was HtrA1 switch 10 (BBa_K4165030) since it was the best switch fulfilling the criteria of structure assessment, docking, and RMSD.
Mathematical modeling
Transcription rate and translation rate
the mathematical modeling was based on our code for the calculation of transcription and translation (you can find it in the code section) besides the estimated results from the wet lab.
Figure 4 shows the results from the transcription and translation code showing the
variation of mRNA and protein concentrations.
References
1. Goedert, M., & Spillantini, M. G. (2017). Propagation of Tau aggregates. Molecular Brain, 10. https://doi.org/10.1186/s13041-017-0298-7
2. Etienne, M. A., Edwin, N. J., Aucoin, J. P., Russo, P. S., McCarley, R. L., & Hammer, R. P. (2007). Beta-amyloid protein aggregation. Methods in molecular biology (Clifton, N.J.), 386, 203–225. https://doi.org/10.1007/1-59745-430-3_7
4. Seidler, P., Boyer, D., Rodriguez, J., Sawaya, M., Cascio, D., Murray, K., Gonen, T., & Eisenberg, D. (2018). Structure-based inhibitors of tau aggregation. Nature chemistry, 10(2), 170. https://doi.org/10.1038/nchem.2889
5. Romero-Molina, S., Ruiz-Blanco, Y. B., Mieres-Perez, J., Harms, M., Münch, J., Ehrmann, M., & Sanchez-Garcia, E. (2022). PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein–Peptide and Protein–Protein Binding Affinity. Journal of Proteome Research.
6. Stein, V., & Alexandrov, K. (2014). Protease-based synthetic sensing and signal amplification. Proceedings of the National Academy of Sciences, 111(45), 15934-15939.
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