Difference between revisions of "Part:BBa K4165059"

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===Dry-Lab characterization===
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===Dry-lab Characterization===
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<p><img src="https://static.igem.wiki/teams/4165/wiki/parts-registry/39-2.jpg" style="margin-left:300px;" alt="" width="400" /></p>
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                                        Figure 1. The 3D structure of switch 39 modelled by tRrosseta.
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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
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<h1>Switch construction Pipeline</h1>
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<p><img src="https://static.igem.wiki/teams/4165/wiki/registry/dry-lab-modelling-pipeline.png" style="margin-left:200px;" alt="" width="500" /></p>
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                    Figure 2. A figure which dsecribes our Dry-Lab Modelling Pipeline. By team CU_Egypt 2022.
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<html>
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<p style=" font-weight: bold; font-size:14px;"> 1) Modelling </p>
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<p> 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.</p>
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<p style=" font-weight: bold; font-size:14px;"> 2) Structure Assessment </p>
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<p>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: <a href="https://2022.igem.wiki/cu-egypt/ProteinModelling.html"> Modelling</a>.</p>
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<p style=" font-weight: bold; font-size:14px;"> 3) Quality Assessment </p>
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<p>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: <a href="https://2022.igem.wiki/cu-egypt/software.html"> Software</a> under the name of Modric.</p>
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<p style=" font-weight: bold; font-size:14px;">4) Filtering</p>
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<p>We take the top ranked models from the previous steps.</p>
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<p style=" font-weight: bold; font-size:14px;">5) Docking</p>
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<p>The top models of inhibitor and HTRA Binding Peptide are docked with HtrA1 (BBa_K4165004).</p>
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<p style=" font-weight: bold; font-size:14px;">6) Ranking</p>
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<p>The docking results are ranked according to their PRODIGY results. For more information: <a href="https://2022.igem.wiki/cu-egypt/Docking.html"> Docking</a>.</p>
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<p style=" font-weight: bold; font-size:14px;">7) Top Models</p>
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<p>The results that came out from PRODIGY are ranked and top models are chosen to proceed with to the next step. For more information: <a href="https://2022.igem.wiki/cu-egypt/Docking.html"> Docking</a>.</p>
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<p style=" font-weight: bold; font-size:14px;">8) Alignment</p>
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<p>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.</p>
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<p><img src="https://static.igem.wiki/teams/4165/wiki/parts-registry/switches/switch31/picture10.png" style="margin-left:300px;" alt="" width="300" /></p>
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                    Figure 3. Aligned structures of HtrA1 binding peptide 1 docked to HtrA1 and inhibitor docked to HtrA1.
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<p style=" font-weight: bold; font-size:14px;">9) Linker length</p>
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<p>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</p>
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<p style=" font-weight: bold; font-size:14px;">10) Assembly</p>
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<p>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.</p>
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<p>a<img src="https://static.igem.wiki/teams/4165/wiki/parts-registry/switches/switch31/ninhiibitor31-clamp.png" style="margin-left:50px;" alt="" width="150" />b<img src="https://static.igem.wiki/teams/4165/wiki/parts-registry/htra1-bp/h1b.jpg" style="margin-left:50px;" alt="" width="150" />c<img src="https://static.igem.wiki/teams/4165/wiki/q8iub5-trrosetta-model3.png" style="margin-left:50px;" alt="" width="150" /></p>
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    Figure 4. a) Seed_GGSGGGGG_seed clamp b) HTRA Binding Peptide 1 c)WAP-four disulfide core domain 13 serine protease inhibitor.
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<html>
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<p style=" font-weight: bold; font-size:14px;">11) Structure Assessment</p>
 +
<p>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: <a href="https://2022.igem.wiki/cu-egypt/ProteinModelling.html"> Modeling</a>.</p>
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 +
<p style=" font-weight: bold; font-size:14px;">12) Quality Assessment </p>
 +
<p>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: <a href="https://2022.igem.wiki/cu-egypt/software.html"> Software</a> under the name of Modric.</p>
  
<p style=" font-weight: bold; font-size:14px;"> Modeling </p>
 
  
The switch was modeled by (Alphafold - Rosettafold - tRrosetta) and the top model was obtained from tRrosseta.
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<p style=" font-weight: bold; font-size:14px;">13) Ranking</p>
 +
<p>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: <a href="https://2022.igem.wiki/cu-egypt/software.html"> Software</a> under the name of Abu Trika.</p>
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<p style=" font-weight: bold; font-size:14px;">Table 1. quality assessment parameters of switch 39.</p>
 
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<p style=" font-weight: bold; font-size:14px;">14) Alignment</p>
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<p>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.</p>
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<p style=" font-weight: bold; font-size:14px;">Table 2. RMSD calculated from alignment of switch 32 and its basic parts.</p>
 
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<p><img src="https://static.igem.wiki/teams/4165/wiki/parts-registry/39-2.jpg" style="margin-left:220px;" alt="" width="500" /></p>
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table, th, td {
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<table style="width:65%">
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  <tr>
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    <th>average RMSD from free HtrA binding peptide1</th>
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    <th>average RMSD from docked HtrA binding peptide1</th>
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    <th>RMSD from free seed peptide</th>
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  </tr>
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  <tr>
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    <td>1.58175</td>
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    <td>1.66</td>
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    <td>6.529</td>
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  </tr>
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</table>
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                                Figure 1. The 3D structure of switch 39 modelled by tRrosseta.
 
  
 
===Refernces===
 
===Refernces===

Revision as of 23:31, 13 October 2022


HtrA1 Switch 39

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_K4165017), seed peptide (BBa_K4165012), GS Linker (BBa_K4165019), seed peptide (BBa_K4165012), GS Linker (BBa_K4165017), WAP inhibitor (BBa_K4165008), and T7 terminator (BBa_K731721).


Usage and Biology

Switch 39 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


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    INCOMPATIBLE WITH RFC[12]
    Illegal NheI site found at 589
  • 21
    COMPATIBLE WITH RFC[21]
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    INCOMPATIBLE WITH RFC[25]
    Illegal NgoMIV site found at 572
    Illegal AgeI site found at 308
  • 1000
    INCOMPATIBLE WITH RFC[1000]
    Unknown


Dry-lab Characterization

                                       Figure 1. The 3D structure of switch 39 modelled by tRrosseta.


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 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.

cbeta_deviations clashscore molprobity ramachandran_favored ramachandran_outliers Qmean_4 Qmean_6
0 2.17 1.56 89.23 1.54 -1.33041 -1.75356


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.

Table 2. RMSD calculated from alignment of switch 32 and its basic parts.

average RMSD from free HtrA binding peptide1 average RMSD from docked HtrA binding peptide1 RMSD from free seed peptide
1.58175 1.66 6.529

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