Part:BBa_K4165035
HtrA1 Switch number 15
This composite part consists of T7 promoter (BBa_K3633015), lac operator (BBa_K4165062), pGS-21a RBS (BBa_K4165016), 6x His-tag (BBa_K4165020), WAP inhibitor (BBa_K4165008), GSGSGS linker (BBa_J18921), TD28rev (BBa_K4165006), GGSGGGGG linker (BBa_K4165019), WWW (BBa_K4165007), GSGSGS linker (BBa_J18921), H1A (BBa_K4165000) and T7 terminator (BBa_K731721).
Usage and Biology
Switch 15 is used to mediate the activity of HTRA1. It is composed of 3 parts connected by different linkers; an HtrA1 peptide binding PDZ, a clamp of two targeting peptides for tau or amyloid beta, and a catalytic domain inhibitor. Activating HTRA1 upon clamp binding to the target protein 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 seed peptide is considered as an amyloid binding peptide and is proved experimentally to inhibit the aggregations of amyloid beta through cell viability assays with a survival rate values nearly 100%. The H1A peptide was validated 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 accoding 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]
- 25INCOMPATIBLE WITH RFC[25]Illegal NgoMIV site found at 379
Illegal AgeI site found at 115 - 1000COMPATIBLE WITH RFC[1000]
Modeling
The switch was modeled by (Alphafold - Rosettafold - tRrosetta) and the top model was obtained from tRrosseta with a score of 4 out of 6 according to our quality assessment code.
Dry-lab Characterization
Figure 1. A figure which dsecribes our Dry-Lab Modelling Pipeline. By team CU_Egypt 2022.
Figure 2. The 3D structure of switch 15 is modeled by TRrosetta. Red: Tau binding peptides,
blue: H1A peptide, cyan: inhibitor and green: linkers
Docking
switch 15 vs HtrA1 trimer:
ΔG = -15.52
Figure 3. The 3D structure of switch 15 docked to HtrA1 displayed on Pymol.
Mathematical modeling
Transcription rate and translation rate under T7 promoter
the mathematical modeling was based on our code for the calculation of transcription and translation (you can find it in the code section) beside with the estimated results from the wet lab.
Figure 4. this figure shows the results from the transcription and translation code showing the
variation of mRNA and protein concentrations with time.
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 modelling (ab initio) and template-based modelling. For modelling 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 modelling 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 intereset (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 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 binded to the same site or not.
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.
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.
a
b
c
Figure (a,b,c) : 3D structure of Q8IUB5 Inhibitor , H1A Peptide , and TD28rev-GGSGGGG-WWW clamp
used in our assembly of switch 15
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.
| cbeta_deviations | clashscore | molprobity | ramachandran_favored | ramachandran_outliers | Qmean_4 | Qmean_6 |
|---|---|---|---|---|---|---|
| 1 | 4.23 | 1.21 | 98.56 | 0.72 | 1.034211 | 0.034805 |
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.
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 |
|---|---|
| 1.31 | 10.14 |
Refernces
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
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