Difference between revisions of "Part:BBa K4365006:Design"

 
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===Design Notes===
 
===Design Notes===
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codon-optimized for S. cerevisiae using the codon optimization tool on GenScript.
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Codon-optimized for <i>S. cerevisiae</i> using the codon optimization tool on GenScript.
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===Source===
 
===Source===
  
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Literature
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The sequences of the hydrophobic signal peptide was collected from literature <ref>N J Talbot, D J Ebbole, J E Hamer (1993) Identification and characterization of MPG1, a gene involved in pathogenicity from the rice blast fungus Magnaporthe grisea, The Plant Cell, Volume 5, Issue 11, Pages 1575–1590, https://doi.org/10.1105/tpc.5.11.1575</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>.
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https://academic.oup.com/plcell/article/5/11/1575/5984612?login=true
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===References===
 
===References===

Latest revision as of 13:53, 12 October 2022


Signal peptide of MPGI from Magnaporthe grisea


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-optimized for S. cerevisiae using the codon optimization tool on GenScript.

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. ↑ N J Talbot, D J Ebbole, J E Hamer (1993) Identification and characterization of MPG1, a gene involved in pathogenicity from the rice blast fungus Magnaporthe grisea, The Plant Cell, Volume 5, Issue 11, Pages 1575–1590, https://doi.org/10.1105/tpc.5.11.1575
  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