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Context Influences on TALE-DNA Binding Revealed by Quantitative Profiling
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Details:
  • Pubmed ID:
    26067805
  • Pubmed Central ID:
    PMC4467457
  • Description:
    Transcription activator-like effector (TALE) proteins recognize DNA using a seemingly simple DNA-binding code, which makes them attractive for use in genome engineering technologies that require precise targeting. Although this code is used successfully to design TALEs to target specific sequences, off-target binding has been observed and is difficult to predict. Here we explore TALE-DNA interactions comprehensively by quantitatively assaying the DNA-binding specificities of 21 representative TALEs to ∼5,000-20,000 unique DNA sequences per protein using custom-designed protein-binding microarrays (PBMs). We find that protein context features exert significant influences on binding. Thus, the canonical recognition code does not fully capture the complexity of TALE-DNA binding. We used the PBM data to develop a computational model, Specificity Inference For TAL-Effector Design (SIFTED), to predict the DNA-binding specificity of any TALE. We provide SIFTED as a publicly available web tool that predicts potential genomic off-target sites for improved TALE design.

  • Document Type:
  • Collection(s):
  • Funding:
    DP1 GM105378/DP/NCCDPHP CDC HHS/United States
    R01 GM113708/GM/NIGMS NIH HHS/United States
    R01 HG004037/HG/NHGRI NIH HHS/United States
    R21 HG007573/HG/NHGRI NIH HHS/United States
    R21 HG007573/HG/NHGRI NIH HHS/United States
    T32 HG002295/HG/NHGRI NIH HHS/United States
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