Difference between revisions of "Part:BBa K4165021"

(→‎Dry lab)
(→‎Dry-lab Characterization)
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<h1>Modelling Pipeline</h1>
 
<h1>Modelling Pipeline</h1>
  
−
<h2>1) Modelling:</h2>
+
<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> 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>
−
<h2>2) Structure Assessment:</h2>
+
<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: (Link modelling page).</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: (Link modelling page).</p>
−
<h2>3) Quality Assessment:</h2>
+
<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>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>
−
<h2>4) Filtering:</h2>
+
<p style=" font-weight: bold; font-size:14px;">4) Filtering</p>
 
<p>We take the top ranked models from the previous steps.</p>
 
<p>We take the top ranked models from the previous steps.</p>
−
<h2>5) Docking:</h2>
+
<p style=" font-weight: bold; font-size:14px;">5) Docking</p>
 
<p>The top models are docked with the protein of intereset (in our case it was the HtrA1 with a BBa_K4165004.</p>
 
<p>The top models are docked with the protein of intereset (in our case it was the HtrA1 with a BBa_K4165004.</p>
−
<h2>6) Ranking: </h2>
+
<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: (Link Docking page).</p>
 
<p>The docking results are ranked according to their PRODIGY results. For more information: (Link Docking page).</p>
−
<h2>7) Top Models: </h2>
+
<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>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>
−
<h2>8) Alignment: </h2>
+
<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>
 
<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>
−
<h2>9) Linker length: </h2>
+
<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.</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.</p>
−
<h2>10) Assembly: </h2>
+
<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>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>
  
−
<h2>11) Structure Assessment: </h2>
+
<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: (Link modelling page).</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: (Link modelling page).</p>
−
<h2>12) Quality Assessment: </h2>
+
<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: (Link software page) under the name of Modric.</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>
−
<h2>13) Ranking: </h2>
+
<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 Modric.</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>
−
<h2>14) Alignment: </h2>
+
<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>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>
  
Line 103: Line 103:
 
                     Figure 1. A figure which dsecribes our Dry-Lab pipeline
 
                     Figure 1. A figure which dsecribes our Dry-Lab pipeline
 
    By team CU_Egypt 2022.
 
    By team CU_Egypt 2022.
 +
===Conclusion===
 +
The top model was HtrA1 switch 12 (BBa_K4165032) since it was the best switch fulfilling the criteria of structure assessment, docking, and RMSD.
 +
 
===Conclusion===
 
===Conclusion===
 
The top model was HtrA1 switch 12 (BBa_K4165032) since it was the best switch fulfilling the criteria of structure assessment, docking, and RMSD.
 
The top model was HtrA1 switch 12 (BBa_K4165032) since it was the best switch fulfilling the criteria of structure assessment, docking, and RMSD.

Revision as of 14:50, 13 October 2022


HtrA1 switch 1

This composite part consists of T7 promoter (BBa_K3633015), lac operator (BBa_K4165062), pGS-21a RBS (BBa_K4165016), 6x His-tag (BBa_K4165020), SPINK8 Inhibitor (BBa_K4165010), TD28rev (BBa_K4165006), WWW (BBa_K4165007), H1A peptide (BBa_K4165000) and T7 terminator (BBa_K731721).


Usage and Biology

Switch 1 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 TD28REV and WWW peptides which are considered as tau binding peptides are proved experimentally to bind with tau inhibit the aggregations of tau aggregations respectively. 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. The process of assembly of the whole switch was done accoding to both CAPRI and NCBI protocols.


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
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]

Dry-lab Characterization

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.1: This figure shows the Switch 1 top model by Pymol visualization.

Table 1: Quality assessment parameters of Switch 1 model.

cbeta_deviations clashscore molprobity ramachandran_favored ramachandran_outliers Qmean_4 Qmean_6
0 4.23 1.2 99.3 0.7 0.384058 -0.7745


Modelling 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.

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.

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.




                   Figure 1. A figure which dsecribes our Dry-Lab pipeline

By team CU_Egypt 2022.

Conclusion

The top model was HtrA1 switch 12 (BBa_K4165032) since it was the best switch fulfilling the criteria of structure assessment, docking, and RMSD.

Conclusion

The top model was HtrA1 switch 12 (BBa_K4165032) since it was the best switch fulfilling the criteria of structure assessment, docking, and RMSD.

Conclusion

The top model was HtrA1 switch 12 (BBa_K4165032) since it was the best switch fulfilling the criteria of structure assessment, docking, and RMSD.

References

1. Goedert, M., & Spillantini, M. G. (2017). Propagation of Tau aggregates. Molecular Brain, 10. https://doi.org/10.1186/s13041-017-0298-7

2. Etienne, M. A., Edwin, N. J., Aucoin, J. P., Russo, P. S., McCarley, R. L., & Hammer, R. P. (2007). Beta-amyloid protein aggregation. Methods in molecular biology (Clifton, N.J.), 386, 203–225. https://doi.org/10.1007/1-59745-430-3_7

4. Seidler, P., Boyer, D., Rodriguez, J., Sawaya, M., Cascio, D., Murray, K., Gonen, T., & Eisenberg, D. (2018). Structure-based inhibitors of tau aggregation. Nature chemistry, 10(2), 170. https://doi.org/10.1038/nchem.2889

5. 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.

6. Stein, V., & Alexandrov, K. (2014). Protease-based synthetic sensing and signal amplification. Proceedings of the National Academy of Sciences, 111(45), 15934-15939