Latest revision as of 03:30, 14 October 2022

HtrA1 Switch 31

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_K4165066), seed peptide (BBa_K4165012), GS Linker (BBa_K4165019), seed peptide (BBa_K4165012), GS Linker (BBa_K4165066), WAP inhibitor (BBa_K4165008), and T7 terminator (BBa_K731721).

Usage and Biology

Switch # 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 according to both CAPRI and NCBI protocols.

Sequence and Features

Dry-lab Characterization

                               Figure 1.: Top 3D Model of switch 31 displayed in Pymol 

This switch was modeled by (Alphafold - Rosettafold - tRrosetta) and the top model was obtained from tRrosseta. the pipline 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 12.8 and 24.7 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 bc

  Figure 4. a) Seed_GGSGGGGG_seed 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 31.

14) 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.

Table 2. RMSD calculated from alignment of switch 31 and its basic parts.

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. 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