Difference between revisions of "Part:BBa K4165193"

(Usage and Biology)
 
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===<span class='h3bb'>Sequence and Features</span>===
 
===<span class='h3bb'>Sequence and Features</span>===
 
<partinfo>BBa_K4165193 SequenceAndFeatures</partinfo>
 
<partinfo>BBa_K4165193 SequenceAndFeatures</partinfo>
 
===Features and codon optimize===
 
This sequence is optimized for E. coli bacteria
 
 
===Source===
 
Homo sapiens Amyloid-beta precursor protein - UniProt (P05067)
 
  
  
 
===Dry lab===
 
===Dry lab===
 
<p style=" font-weight: bold; font-size:14px;"> Modeling </p>
 
<p style=" font-weight: bold; font-size:14px;"> Modeling </p>
Due to the nonavailability of a model to this peptide we modeled it in our used modeling software's and after the Running of the quality assessment, the models have been scored to get the top model.
+
 
 +
The peptide was modeled by several software (Alphafold-Apptest-Pepfold) and the best model from Apptest scored 3 out of 6 according to our Quality assessment code.
  
 
<html>
 
<html>

Latest revision as of 19:40, 11 October 2022


Amyloid beta peptide 13 (Aβ 32-42)

This part encodes a part of the Amyloid 𝛽 fragment (32-42) which has the ability to bind to A𝛽 plaques inside the brain.


Usage and Biology

Segments of amyloid beta fibrils are widely used as a recognition sequence for amyloid beta plaques inside the brain, this is due to the homotypic interactions in the C-terminus of fibrils. This peptide starts from amino acids 32 to 42 of the fibril.

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

Modeling

The peptide was modeled by several software (Alphafold-Apptest-Pepfold) and the best model from Apptest scored 3 out of 6 according to our Quality assessment code.

                             Figure 1.: Amyloid-beta (32-42) top model visualized by Pymol.


References

1- Zhao, Y., Cai, J., Liu, Z., Li, Y., Zheng, C., Zheng, Y., ... & Liu, Y. (2018). Nanocomposites inhibit the formation, mitigate the neurotoxicity, and facilitate the removal of β-amyloid aggregates in Alzheimer’s disease mice. Nano letters, 19(2), 674-683.