Difference between revisions of "Part:BBa K5102061:Design"
 
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===Design Notes===
 
===Design Notes===
  
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The synthetic UTR was designed utilizing the deep learning model developed by Castillo-Hair et al., which optimizes 5’ UTRs for efficient mRNA translation using generative neural networks and gradient descent (refer to the model wiki for more details). This model was trained on polysome profiling data from randomized 5’ UTR libraries across multiple cell types, allowing it to learn sequence features that enhance translation efficiency.
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Since the part was obtained from the lab, the design considerations focused on ensuring proper incorporation into plasmids for expression and verifying compatibility with Biobrick standards.
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The model was validated by calculation of the mean ribosome load (MRL) and minimum free energy (MFE) for each designed UTR.
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===References===
 
===References===
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Castillo-Hair, S. et al. Optimizing 5’UTRs for mRNA-delivered gene editing using deep learning. Nat Commun 15, 5284 (2024).
 

Latest revision as of 00:56, 1 October 2024


mTagBFP2


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
    INCOMPATIBLE WITH RFC[1000]
    Illegal BsaI.rc site found at 634
    Illegal SapI.rc site found at 16


Design Notes

Since the part was obtained from the lab, the design considerations focused on ensuring proper incorporation into plasmids for expression and verifying compatibility with Biobrick standards.


Source

The part has been amplified from an already existing plasmid in the lab.

References