Latest revision as of 20:15, 13 October 2022

HtrA1 Switch number 18

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), seed peptide (BBa_K4165012), GGSGGGGG linker (BBa_K4165019), seed peptide (BBa_K4165012), GSGSGS linker (BBa_J18921), H1A (BBa_K4165000) and T7 terminator (BBa_K731721).

Usage and Biology

Switch 18 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.

This part or its concept can be used by next iGEM teams to use in constructing target specific switchable protease system specially with HtrA1 protease.

Sequence and Features

Dry Lab

Dry-lab Characterization

                   Figure 1. A figure which dsecribes our Dry-Lab Modelling Pipeline. By team CU_Egypt 2022.

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.

                  Figure 2. The 3D structure of switch 18 modeled by TRrosetta. Red: Tau binding peptides, 
                                  blue: H1A peptide, cyan: inhibitor, and green: linkers

Docking

switch 18 vs HtrA1:

The binding affinity of switch 18 to HtrA1 is calculated as ΔG = -19.516 kcal/mol.

                      Figure 2. The 3D structure of switch 18 docked to HtrA1 Visualized by 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 3. 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.

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        Figure (a,b,c) : 3D structure of  Q8IUB5 Inhibitor , H1A Peptide ,  and seed-GGSGGGGG-seed 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.

Functional Parameters

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.

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