Part:BBa_K4165026
HtrA1 switch 6
This composite part consists of T7 promoter (BBa_K3633015), lac operator (BBa_K4165062), pGS-21a RBS (BBa_K4165016), 6x His-tag (BBa_K4165020), SPINK8 Inhibitor (BBa_K4165010), seed peptide (BBa_K4165012), H1A peptide (BBa_K4165000) and T7 terminator (BBa_K731721).
Usage and Biology
Switch 6 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. The process of assembly of the whole switch was done according to both CAPRI and NCBI protocols.
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.:3D Predicted Structure of Switch 6 Protein by Pymol Visualization.
Figure 2. A figure which describes our Dry-Lab Modelling Pipeline. By team CU_Egypt 2022.
Switch construction Pipeline
1) Modelling
Since our parts do not have experimentally acquired structures, we have to model them. 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 our structures we used the Swiss-Model tool which gives an overall on quality of any 3D structure (For more information: (Link 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 For more information: (Link software page) under the name of Modric.
4) Filtering
We take the top-ranked models from the previous steps.
5) Docking
The top models are docked with the protein of interest (in our case it was the HtrA1 with a BBa_K4165004.
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 3. 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 |
|---|---|---|---|
| 5.558 | 6.225 | 5.569 | 5.850 |
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 on quality of any 3D structure (For more information: (Link modelling 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: (Link software page) under the name of Modric.
13) Ranking
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: (Link software page) under the name of Modric.
The switch was modeled by (Alphafold - Rosettafold - tRrosetta) and the top model was obtained from tRrosseta with a score of 6 out of 6 according to our quality assessment code.
| cbeta_deviations | clashscore | molprobity | ramachandran_favored | ramachandran_outliers | Qmean_4 | Qmean_6 |
|---|---|---|---|---|---|---|
| 0 | 2.12 | 0.98 | 100 | 0 | -0.15579 | -1.5803 |
Table 2: Quality assessment parameters of Switch 6 model.
14) Alignment
The docked structures are then aligned and compared to the basic parts which are docked with protein of interest (HtrA1). The structures with least RMSD are chosen.
| RMSD Before Docking | RMSD After Docking |
|---|---|
| 1.273 | 1.581 |
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.
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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