Revision as of 23:53, 13 October 2022

HtrA1 Switch 38

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_K4165068), seed peptide (BBa_K4165012), GS Linker (BBa_K4165019), seed peptide (BBa_K4165012), GS Linker (BBa_K4165068), WAP inhibitor (BBa_K4165008), and T7 terminator (BBa_K731721).

Usage and Biology

Switch 38 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 seed peptide which is considered as an amyloid binding peptide 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 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

Dry-lab Characterization

                                       Figure 1. The 3D structure of switch 38 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 dsecribes our Dry-Lab Modelling Pipeline. By team CU_Egypt 2022.

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: Modelling.

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: Software under the name of Modric.

4) Filtering

We take the top ranked models from the previous steps.

5) Docking

The top models of inhibitor and HTRA Binding Peptide are docked with HtrA1 (BBa_K4165004).

6) Ranking

The docking results are ranked according to their PRODIGY results. For more information: Docking.

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: Docking.

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.

                   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 which is between both C terminals, N terminals, C- and N- terminal, and N- and C-terminals. The distance was measured to be between 13 and 25 angstrom

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.

abc

   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 on quality of any 3D structure (For more information: Modeling.

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: Software 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: Software under the name of Abu Trika.

Table 1. quality assessment parameters of switch 39.

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