Difference between revisions of "Part:BBa K4365016:Design"

 
 
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===Source===
 
===Source===
 
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The sequences of the hydrophobic signal peptide was collected from literature <ref>Denis Tagu, Birgit Nasse, Francis Martin (1996) Cloning and characterization of hydrophobins-encoding cDNAs from the ectomycorrhizal Basidiomycete Pisolithus tinctorius, Gene Volume 168, Issue 1, Pages 93-97 https://doi.org/10.1016/0378-1119(95)00725-3</ref> and was extracted via analysis of their sequence using the SignalP - 5.0 signal peptide predictor tool <ref>José Juan Almagro Armenteros et al. (2019) SignalP 5.0 improves signal peptide predictions using deep neural networks Nature Biotechnology, 37, 420-423, doi: 10.1038/s41587-019-0036-z </ref>.
Literature: https://link.springer.com/content/pdf/10.1007/s002940050454.pdf
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===References===
 
===References===

Latest revision as of 14:16, 12 October 2022


Signal peptide of Aa-PRI2 from Agrocybe aegerita


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]


Design Notes

Codon optimised for yeast.


Source

The sequences of the hydrophobic signal peptide was collected from literature [1] and was extracted via analysis of their sequence using the SignalP - 5.0 signal peptide predictor tool [2].

References

  1. Denis Tagu, Birgit Nasse, Francis Martin (1996) Cloning and characterization of hydrophobins-encoding cDNAs from the ectomycorrhizal Basidiomycete Pisolithus tinctorius, Gene Volume 168, Issue 1, Pages 93-97 https://doi.org/10.1016/0378-1119(95)00725-3
  2. José Juan Almagro Armenteros et al. (2019) SignalP 5.0 improves signal peptide predictions using deep neural networks Nature Biotechnology, 37, 420-423, doi: 10.1038/s41587-019-0036-z