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CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria.

Cameron T Roots1, Jeffrey E Barrick1

  • 1Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas 78712, U.S.A.

Biorxiv : the Preprint Server for Biology
|September 16, 2024
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Summary

CryptKeeper software predicts unintended gene expression from synthetic DNA, preventing genetic instability and experimental failures in molecular biology. This tool aids researchers in designing experiments and avoiding complications from cryptic gene expression.

Keywords:
computational DNA sequence designdesign-build-test cycleplasmid instabilityrecombinant protein overexpressionreliability and reproducibility

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Area of Science:

  • Molecular Biology
  • Synthetic Biology
  • Bioinformatics

Background:

  • Molecular biology techniques like gene cloning and protein expression involve introducing synthetic DNA into organisms.
  • Unintended recognition of these sequences by cellular machinery can lead to cryptic gene expression.
  • Cryptic gene expression causes genetic instability and masks experimental results, complicating research.

Purpose of the Study:

  • To introduce CryptKeeper, a computational pipeline for visualizing bacterial gene expression signals and translational burden from DNA sequences.
  • To address the challenge of contextualizing predictions from individual computational tools for gene expression elements.
  • To mitigate cloning challenges and experimental failures caused by unintentional gene expression.

Main Methods:

  • Developed CryptKeeper, a software pipeline predicting bacterial gene expression signals and translational burden.
  • Applied CryptKeeper to analyze published examples of cryptic gene expression in *E. coli*.
  • Investigated cryptic gene expression from eukaryotic virus infectious clones and individual proteins.

Main Results:

  • CryptKeeper accurately predicted unwanted gene expression leading to genetic instability in published cases.
  • The software identified off-target expression elements causing protein truncations and hindering purification.
  • CryptKeeper's predictions correlated with experimental failures attributed to cryptic gene expression.

Conclusions:

  • CryptKeeper effectively visualizes and estimates translational burden from DNA sequences, aiding in the detection of cryptic gene expression.
  • Incorporating negative design with CryptKeeper into synthetic biology and reverse genetics workflows can prevent experimental complications.
  • This tool helps researchers avoid unexplained failures and challenges arising from unintentional gene expression during DNA manipulation.