Difference between revisions of "Part:BBa K4165180"
 
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This part encodes an Amyloid 𝛽 peptide which has the ability to bind to A𝛽 plaques inside the brain.
 
This part encodes an Amyloid 𝛽 peptide which has the ability to bind to A𝛽 plaques inside the brain.
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For the prediction of 3D structure, the peptide was modeled through three peptide modeling software Apptest, Alphafold2, and Pepfold3, followed by ranking them according to our pipeline parameters. Most of the models for this peptide ranked 6 out of 6, with the top-ranked model being (Alphafold model 3).
 
For the prediction of 3D structure, the peptide was modeled through three peptide modeling software Apptest, Alphafold2, and Pepfold3, followed by ranking them according to our pipeline parameters. Most of the models for this peptide ranked 6 out of 6, with the top-ranked model being (Alphafold model 3).
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<span class='h3bb'>Sequence and Features</span>
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<partinfo>BBa_K4165180 SequenceAndFeatures</partinfo>
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===Functional Parameters===
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Overall charge: +9.0
  
 
===Dry Lab Characterization===
 
===Dry Lab Characterization===
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For the prediction of 3D structure, the peptide was modeled through three peptide modeling software (Apptest, Alphafold2, and Pepfold3) followed by ranking them according to our pipeline parameters. Most of the models for this peptide ranked 6 out of 6, with the top-ranked model being (Alphafold model 3).  
 
For the prediction of 3D structure, the peptide was modeled through three peptide modeling software (Apptest, Alphafold2, and Pepfold3) followed by ranking them according to our pipeline parameters. Most of the models for this peptide ranked 6 out of 6, with the top-ranked model being (Alphafold model 3).  
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<html>
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<style>
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table, th, td {
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  border:1px solid black; margin-left:auto;margin-right:auto;
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}
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</style>
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<body>
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<table style="width:65%">
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<table>
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  <tr>
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    <th>cbeta_deviations</th>
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    <th>clashscore</th>
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    <th>molprobity</th>
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    <th>ramachandran_favored</th>
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    <th>ramachandran_outliers</th>
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    <th>Qmean_4</th>
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    <th>Qmean_6</th>
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  </tr>
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  <tr>
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    <td>0</td>
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    <td>0</td>
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    <td>0.5</td>
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    <td>100</td>
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    <td>0</td>
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    <td>-2.041544</td>
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    <td>-2.009539</td>
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  </tr>
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</table>
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</body>
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</html>
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<p><img src="https://static.igem.wiki/teams/4165/wiki/parts-registry/picture1.png" style="margin-left:200px;" alt="" width="500" /></p>
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<p><img src="https://static.igem.wiki/teams/4165/wiki/dry-lab/molecular-dynamics/picture1.jpg" style="margin-left:200px;" alt="" width="500" /></p>
 
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                                       Figure 1.: Predicted 3D structure of R8 peptide
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                                       Figure 1.: Predicted 3D structure of R8 peptide.
  
  
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===Functional Parameters===
 
  
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Overall charge: +9.0
 
  
  
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===References===
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<span class='h3bb'>Sequence and Features</span>
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<partinfo>BBa_K4165180 SequenceAndFeatures</partinfo>
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Mamsa, S. S., & Meloni, B. P. (2021). Arginine and Arginine-Rich Peptides as Modulators of Protein Aggregation and Cytotoxicity Associated With Alzheimer’s Disease. Frontiers in Molecular Neuroscience. https://doi.org/10.3389/fnmol.2021.759729
  
 
<partinfo>BBa_K4165180 parameters</partinfo>
 
<partinfo>BBa_K4165180 parameters</partinfo>
 
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Latest revision as of 13:27, 13 October 2022


Amyloid beta peptide (R8)

This part encodes an Amyloid 𝛽 peptide which has the ability to bind to A𝛽 plaques inside the brain.


Usage and Biology

Arginine (R) is a basic amino acid with the presence of a guanidino group at its aliphatic side chain. It is typically protonated at physiological pH where the guanidino group turns into a cationic guanidinium moiety that is highly stable and able to self-associate and cluster. These properties contribute to the intra- and intermolecular associations of arginine residues, as it provides a great capacity for electrostatic interactions (especially hydrogen-bonding) that results in a tendency to form stable clusters in solution.

Arginine has long been recognized as a chemical chaperone, with its ability to interact with and influence proteins in solution. In silico experiments have proved its ability to bind protein surfaces for a long time through its carboxyl and guanidinium groups, and form clusters through self-association with other arginine molecules. This has led to various in vitro experiments that proved the ability of arginine to suppress protein aggregations, which made it a very interesting candidate in the modulation of proteopathies correlated with Alzheimer’s disease.

This peptide is composed of eight arginine residues, it is initially designed to bind and inhibit tau and PHF aggregations but proved to not affect them when alone, only when conjugated to R6 peptide (BBa_K4165159), consequently, we hypothesized it could be useful as an amyloid beta peptide instead.

For the prediction of 3D structure, the peptide was modeled through three peptide modeling software Apptest, Alphafold2, and Pepfold3, followed by ranking them according to our pipeline parameters. Most of the models for this peptide ranked 6 out of 6, with the top-ranked model being (Alphafold model 3).

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]

Functional Parameters

Overall charge: +9.0

Dry Lab Characterization

Modeling

For the prediction of 3D structure, the peptide was modeled through three peptide modeling software (Apptest, Alphafold2, and Pepfold3) followed by ranking them according to our pipeline parameters. Most of the models for this peptide ranked 6 out of 6, with the top-ranked model being (Alphafold model 3).

cbeta_deviations clashscore molprobity ramachandran_favored ramachandran_outliers Qmean_4 Qmean_6
0 0 0.5 100 0 -2.041544 -2.009539 


                                     Figure 1.: Predicted 3D structure of R8 peptide.



References

Mamsa, S. S., & Meloni, B. P. (2021). Arginine and Arginine-Rich Peptides as Modulators of Protein Aggregation and Cytotoxicity Associated With Alzheimer’s Disease. Frontiers in Molecular Neuroscience. https://doi.org/10.3389/fnmol.2021.759729