Part:BBa_K4165056
HtrA1 Switch 36
This composite part consists of T7 promoter (BBa_K3633015), lac operator (BBa_K4165062), pGS-21a RBS (BBa_K4165016), 6x His-tag (BBa_K4165020), H1A (BBa_K4165000), GS Linker (BBa_K4165017), TD28rev peptide (BBa_K4165006), GS Linker (BBa_K4165018), WWW peptide (BBa_K4165007), GS Linker (BBa_K4165017), WAP inhibitor (BBa_K4165008), and T7 terminator (BBa_K731721)
Usage and Biology
Switch 36 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.
Sequence and Features
- 10COMPATIBLE WITH RFC[10]
- 12COMPATIBLE WITH RFC[12]
- 21COMPATIBLE WITH RFC[21]
- 23COMPATIBLE WITH RFC[23]
- 25INCOMPATIBLE WITH RFC[25]Illegal NgoMIV site found at 535
Illegal AgeI site found at 271 - 1000COMPATIBLE WITH RFC[1000]
Dry-lab Characterization
Figure 1. The 3D structure of switch 36 modelled by tRrosseta.
This switch was modeled by (Alphafold - Rosettafold - tRrosetta) and the top model was obtained from tRrosseta. the pipeline for generating this model will be discussed in the next section in details
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).
6) Ranking
The docking results are ranked according to the Delta free energy generated by PRODIGY. For more information please check our Docking page.
7) Top Models
The results from PRODIGY are ranked, and the top three models are chosen after the models are visualized to ensure that the proteins interact at the right designated domain to proceed with the next step. For more information please check our Docking page.
8) Alignment
Docked structures are aligned. This means that the HtrA1- binding peptide complex is aligned with the second complex, the HtrA1-inhibitor complex, to check whether they bonded to the same site.
Figure 3. Aligned structures of HtrA1 binding peptide 1 docked to HtrA1 and inhibitor docked to HtrA1.
9) Linker length
The linker lengths are acquired by seeing the distance between the inhibitor and the HtrA1 binding peptide between both C terminals, N terminals, C- and N- terminal, and N- and N- and C-terminals the linker length is calculated to be between 14.5 and 20.6 angstroms.
10) Assembly
After settling on the linkers' lengths, we will now proceed to the assembly step of the whole system, which is done using TRrosetta, AlphaFold, RosettaFold, and Modeller.
a
b
c
Figure 4. a) TD28rev_GGSGGGG_WWW clamp b) HTRA Binding Peptide 1 c) WAP-four disulfide core domain 13 serine protease inhibitor.
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.
Table 1. quality assessment parameters of switch 36.
| cbeta_deviations | clashscore | molprobity | ramachandran_favored | ramachandran_outliers | Qmean_4 | Qmean_6 |
|---|---|---|---|---|---|---|
| 0 | 182.07 | 2.72 | 98.56 | 0.72 | -0.37947 | -1.67069 |
References
1. Lu, J., Cao, Q., Wang, C., Zheng, J., Luo, F., Xie, J., ... & Li, D. (2019). Structure-based peptide inhibitor design of amyloid-β aggregation. Frontiers in molecular neuroscience, 12, 54.
2. 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.
3. Stein, V., & Alexandrov, K. (2014). Protease-based synthetic sensing and signal amplification. Proceedings of the National Academy of Sciences, 111(45), 15934-15939
| None |