Difference between revisions of "Part:BBa K4165052"

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===<span class='h3bb'>Sequence and Features</span>===
 
===<span class='h3bb'>Sequence and Features</span>===
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<partinfo>BBa_K4165051 SequenceAndFeatures</partinfo>
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<partinfo>BBa_K4165052 SequenceAndFeatures</partinfo>
  
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===Dry-lab Characterization===
  
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<p><img src="https://static.igem.wiki/teams/4165/wiki/parts-registry/switches/32-2.jpg" style="margin-left:300px;" alt="" width="400" /></p>
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                                        Figure 1. The 3D structure of switch 32 top model modelled by tRrosetta.
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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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<html>
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<img src="https://static.igem.wiki/teams/4165/wiki/registry/dry-lab-modelling-pipeline.png" style="margin-left:200px;" alt="" width="500" /> <br>
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</html>
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                  Figure 2. A figure which describes 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 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.</p>
 +
<p style=" font-weight: bold; font-size:14px;"> 2) Structure Assessment </p>
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<p>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 
 +
<a href="https://2022.igem.wiki/cu-egypt/ProteinModelling.html">Modeling page</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 out of score 6. For more information: <a href="https://2022.igem.wiki/cu-egypt/ProgrammingClub.html">Programming club page code under the name of Modric.</a>.</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 that have either a score of 5 or 6 </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, and the top models of the clamps are docked with the Target protein, that is, in our case is Beta-amyloid (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 the Delta free energy generated by PRODIGY. For more information please check our <a href="https://2022.igem.wiki/cu-egypt/Docking.html">Docking page</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 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 <a href="https://2022.igem.wiki/cu-egypt/Docking.html">Docking page</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, the HtrA1-inhibitor complex, to check whether they bonded to the same site.</p>
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<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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</html>
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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 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.</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, we will now 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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</html>
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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>
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<p>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 <a href="https://2022.igem.wiki/cu-egypt/ProteinModelling.html">Modeling page</a>.</p>
  
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===Dry-Lab characterization===
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<p style=" font-weight: bold; font-size:14px;">12) 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, please proceed to our <a href="https://2022.igem.wiki/cu-egypt/ProgrammingClub.html">Programming club</a> under the name of Modric.</p>
  
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<p style=" font-weight: bold; font-size:14px;"> Modeling </p>
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<p style=" font-weight: bold; font-size:14px;">Table 1. quality assessment parameters of switch 32.</p>
  
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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;">14) Alignment</p>
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<p>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.</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/switches/32-2.jpg" style="margin-left:200px;" alt="" width="500" /></p>
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<table style="width:65%">
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<table>
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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>2.044</td>
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    <td>1.861</td>
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    <td>6.458</td>
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  </tr>
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</table>
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                            Figure 1. The 3D structure of switch 32 modelled by tRrosetta.
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===Conclusion===
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The top model was HtrA1 switch 10 (BBa_K4165030) since it was the best switch fulfilling the criteria of structure assessment, docking, and RMSD.
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===References===
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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.
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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.
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 +
3. Stein, V., & Alexandrov, K. (2014). Protease-based synthetic sensing and signal amplification. Proceedings of the National Academy of Sciences, 111(45), 15934-15939

Latest revision as of 03:30, 14 October 2022


HtrA1 Switch 32

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_J18921), seed peptide (BBa_K4165012), GS Linker (BBa_K4165019), seed peptide (BBa_K4165012), GS Linker (BBa_J18921), WAP inhibitor (BBa_K4165008), and T7 terminator (BBa_K731721).


Usage and Biology

Switch 32 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.

Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    COMPATIBLE WITH RFC[21]
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    INCOMPATIBLE WITH RFC[25]
    Illegal NgoMIV site found at 508
    Illegal AgeI site found at 244
  • 1000
    COMPATIBLE WITH RFC[1000]

Dry-lab Characterization

                                       Figure 1. The 3D structure of switch 32 top model modelled by tRrosetta.


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 32.

cbeta_deviations clashscore molprobity ramachandran_favored ramachandran_outliers Qmean_4 Qmean_6
0 2.15 1.46 92.31 0.77 -1.05267 -1.3236


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 32 and its basic parts.

average RMSD from free HtrA binding peptide1 average RMSD from docked HtrA binding peptide1 RMSD from free seed peptide
2.044 1.861 6.458

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