Difference between revisions of "Part:BBa K4165044"
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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), GGGGS Linker (BBa_K4165068), Seed peptide (BBa_K4165012), GGSGGGGG Linker (BBa_K4165019), Seed peptide (BBa_K4165012), GGGGS Linker (BBa_K4165068), WAP inhibitor (BBa_K4165008), and T7 terminator (BBa_K731721). | 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), GGGGS Linker (BBa_K4165068), Seed peptide (BBa_K4165012), GGSGGGGG Linker (BBa_K4165019), Seed peptide (BBa_K4165012), GGGGS Linker (BBa_K4165068), WAP inhibitor (BBa_K4165008), and T7 terminator (BBa_K731721). | ||
− | |||
===Usage and Biology=== | ===Usage and Biology=== | ||
+ | Switch 24 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. | ||
+ | |||
<!-- --> | <!-- --> | ||
− | <span class='h3bb'>Sequence and Features</span> | + | ===<span class='h3bb'>Sequence and Features</span>=== |
<partinfo>BBa_K4165044 SequenceAndFeatures</partinfo> | <partinfo>BBa_K4165044 SequenceAndFeatures</partinfo> | ||
+ | |||
+ | ===Dry-lab Characterization=== | ||
+ | <p style=" font-weight: bold; font-size:14px;">Top model</p> | ||
+ | |||
+ | <html> | ||
+ | <p><img src="https://static.igem.wiki/teams/4165/wiki/parts-registry/switch24.png" style="margin-left:220px;" alt="" width="500" /></p> | ||
+ | </html> | ||
+ | |||
+ | Figure 1. The 3D structure of switch 24 modeled by TRrosetta. | ||
+ | |||
+ | |||
+ | |||
+ | The pipeline for creating this model is discussed in details in the section below | ||
+ | |||
+ | <h1>Switch construction Pipeline</h1> | ||
+ | |||
+ | |||
+ | <html> | ||
+ | <img src="https://static.igem.wiki/teams/4165/wiki/registry/dry-lab-modelling-pipeline.png" style="margin-left:200px;" alt="" width="500" /> <br> | ||
+ | |||
+ | |||
+ | |||
+ | <p style="text-align:center;">Figure 2. A figure which dsecribes our Dry-Lab Modelling Pipeline. By team CU_Egypt 2022.</p> | ||
+ | |||
+ | <p style=" font-weight: bold; font-size:14px;"> 1) Modelling </p> | ||
+ | <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> | ||
+ | <p style=" font-weight: bold; font-size:14px;"> 2) 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> | ||
+ | <p style=" font-weight: bold; font-size:14px;"> 3) 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: (Link software page) under the name of Modric.</p> | ||
+ | |||
+ | <p style=" font-weight: bold; font-size:14px;">4) Filtering</p> | ||
+ | <p>We take the top ranked models from the previous steps.</p> | ||
+ | |||
+ | <p style=" font-weight: bold; font-size:14px;">5) Docking</p> | ||
+ | <p>The top models of inhibitor and HTRA Binding Peptide are docked with HtrA1 (BBa_K4165004).</p> | ||
+ | |||
+ | <p style=" font-weight: bold; font-size:14px;">6) Ranking</p> | ||
+ | <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> | ||
+ | |||
+ | <p style=" font-weight: bold; font-size:14px;">7) Top Models</p> | ||
+ | <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: (Link Docking page).</p> | ||
+ | |||
+ | <p style=" font-weight: bold; font-size:14px;">8) Alignment</p> | ||
+ | <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> | ||
+ | |||
+ | <img src="https://static.igem.wiki/teams/4165/wiki/parts-registry/switches/switch31/picture10.png" style="margin-left:300px;" alt="" width="300" /></p> | ||
+ | |||
+ | |||
+ | |||
+ | <p style="text-align:center;"> Figure 3. Aligned structures of HtrA1 binding peptide 1 docked to HtrA1 and inhibitor docked to HtrA1. </p> | ||
+ | <p style=" font-weight: bold; font-size:14px;">9) Linker length</p> | ||
+ | <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 linker length is calculated to be between 13 and 25 angstrom.</p> | ||
+ | |||
+ | <p style=" font-weight: bold; font-size:14px;">10) Assembly</p> | ||
+ | <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> | ||
+ | |||
+ | <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> | ||
+ | |||
+ | |||
+ | Figure 4. a) Seed_GGSGGGGG_seed clamp b) HTRA Binding Peptide 1 c) WAP-four disulfide core domain 13 serine protease inhibitor. | ||
+ | <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> | ||
+ | |||
+ | <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;">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: (Link software page) under the name of Abu Trika.</p> | ||
+ | <p style=" font-weight: bold; font-size:14px;">Table 1. quality assessment parameters of switch 31.</p> | ||
+ | |||
+ | <html> | ||
+ | <style> | ||
+ | table, th, td { | ||
+ | border:1px solid black; margin-left:auto;margin-right:auto; | ||
+ | } | ||
+ | </style> | ||
+ | <body> | ||
+ | <table style="width:65%"> | ||
+ | <table> | ||
+ | <tr> | ||
+ | <th>cbeta_deviations</th> | ||
+ | <th>clashscore</th> | ||
+ | <th>molprobity</th> | ||
+ | <th>ramachandran_favored</th> | ||
+ | <th>ramachandran_outliers</th> | ||
+ | <th>Qmean_4</th> | ||
+ | <th>Qmean_6</th> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td>1</td> | ||
+ | <td>2.18</td> | ||
+ | <td>1.6</td> | ||
+ | <td>87.5</td> | ||
+ | <td>1.56</td> | ||
+ | <td>-3.01105</td> | ||
+ | <td>-3.11268</td> | ||
+ | </tr> | ||
+ | </table> | ||
+ | </body> | ||
+ | </html> | ||
+ | |||
+ | |||
+ | <p style=" font-weight: bold; font-size:14px;">14) Alignment</p> | ||
+ | <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> | ||
+ | |||
+ | |||
+ | |||
+ | <p style=" font-weight: bold; font-size:14px;">References</p> | ||
+ | 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 | ||
+ | |||
+ | 4. 7. Rey, J., Breiden, M., Lux, V., Bluemke, A., Steindel, M., & Ripkens, K. et al. (2022). An allosteric HTRA1-calpain 2 complex with a restricted activation profile. Proceedings Of The National Academy Of Sciences, 119(14). doi: 10.1073/pnas.2113520119<br><br> | ||
+ | |||
+ | 5. Santos, L. H., Ferreira, R. S., & Caffarena, E. R. (2019). Integrating molecular docking and molecular dynamics simulations. In Docking screens for drug discovery (pp. 13-34). Humana, New York, NY. | ||
+ | |||
+ | <p style=" font-weight: bold; font-size:14px;">References </p> | ||
+ | 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 | ||
+ | |||
+ | 4. 7. Rey, J., Breiden, M., Lux, V., Bluemke, A., Steindel, M., & Ripkens, K. et al. (2022). An allosteric HTRA1-calpain 2 complex with a restricted activation profile. Proceedings Of The National Academy Of Sciences, 119(14). doi: 10.1073/pnas.2113520119<br><br> | ||
+ | |||
+ | 5. Santos, L. H., Ferreira, R. S., & Caffarena, E. R. (2019). Integrating molecular docking and molecular dynamics simulations. In Docking screens for drug discovery (pp. 13-34). Humana, New York, NY. | ||
+ | |||
+ | |||
+ | |||
Latest revision as of 03:58, 14 October 2022
HtrA1 Switch 24
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), GGGGS Linker (BBa_K4165068), Seed peptide (BBa_K4165012), GGSGGGGG Linker (BBa_K4165019), Seed peptide (BBa_K4165012), GGGGS Linker (BBa_K4165068), WAP inhibitor (BBa_K4165008), and T7 terminator (BBa_K731721).
Usage and Biology
Switch 24 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
- 10COMPATIBLE WITH RFC[10]
- 12COMPATIBLE WITH RFC[12]
- 21COMPATIBLE WITH RFC[21]
- 23COMPATIBLE WITH RFC[23]
- 25INCOMPATIBLE WITH RFC[25]Illegal NgoMIV site found at 379
Illegal AgeI site found at 115 - 1000COMPATIBLE WITH RFC[1000]
Dry-lab Characterization
Top model
Figure 1. The 3D structure of switch 24 modeled by TRrosetta.
The pipeline for creating this model is discussed in details in the section below
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: Modeling .
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 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: (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.
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 linker length is calculated 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.
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 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: (Link software page) under the name of Abu Trika.
Table 1. quality assessment parameters of switch 31.
cbeta_deviations | clashscore | molprobity | ramachandran_favored | ramachandran_outliers | Qmean_4 | Qmean_6 |
---|---|---|---|---|---|---|
1 | 2.18 | 1.6 | 87.5 | 1.56 | -3.01105 | -3.11268 |
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.
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
4. 7. Rey, J., Breiden, M., Lux, V., Bluemke, A., Steindel, M., & Ripkens, K. et al. (2022). An allosteric HTRA1-calpain 2 complex with a restricted activation profile. Proceedings Of The National Academy Of Sciences, 119(14). doi: 10.1073/pnas.2113520119
5. Santos, L. H., Ferreira, R. S., & Caffarena, E. R. (2019). Integrating molecular docking and molecular dynamics simulations. In Docking screens for drug discovery (pp. 13-34). Humana, New York, NY.
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
4. 7. Rey, J., Breiden, M., Lux, V., Bluemke, A., Steindel, M., & Ripkens, K. et al. (2022). An allosteric HTRA1-calpain 2 complex with a restricted activation profile. Proceedings Of The National Academy Of Sciences, 119(14). doi: 10.1073/pnas.2113520119
5. Santos, L. H., Ferreira, R. S., & Caffarena, E. R. (2019). Integrating molecular docking and molecular dynamics simulations. In Docking screens for drug discovery (pp. 13-34). Humana, New York, NY.