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Context influences on TALE–DNA binding revealed by quantitative profiling

Author

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  • Julia M. Rogers

    (Brigham and Women’s Hospital and Harvard Medical School
    Committee on Higher Degrees in Biophysics, Harvard University)

  • Luis A. Barrera

    (Brigham and Women’s Hospital and Harvard Medical School
    Committee on Higher Degrees in Biophysics, Harvard University
    Harvard Medical School
    Computer Science and Artificial Intelligence Laboratory, MIT)

  • Deepak Reyon

    (Molecular Pathology Unit, Massachusetts General Hospital
    Center for Computational and Integrative Biology, Massachusetts General Hospital
    Center for Cancer Research, Massachusetts General Hospital
    Harvard Medical School)

  • Jeffry D. Sander

    (Molecular Pathology Unit, Massachusetts General Hospital
    Center for Computational and Integrative Biology, Massachusetts General Hospital
    Center for Cancer Research, Massachusetts General Hospital
    Harvard Medical School)

  • Manolis Kellis

    (Computer Science and Artificial Intelligence Laboratory, MIT)

  • J Keith Joung

    (Molecular Pathology Unit, Massachusetts General Hospital
    Center for Computational and Integrative Biology, Massachusetts General Hospital
    Center for Cancer Research, Massachusetts General Hospital
    Harvard Medical School)

  • Martha L. Bulyk

    (Brigham and Women’s Hospital and Harvard Medical School
    Committee on Higher Degrees in Biophysics, Harvard University
    Harvard Medical School
    Brigham and Women’s Hospital and Harvard Medical School)

Abstract

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.

Suggested Citation

  • Julia M. Rogers & Luis A. Barrera & Deepak Reyon & Jeffry D. Sander & Manolis Kellis & J Keith Joung & Martha L. Bulyk, 2015. "Context influences on TALE–DNA binding revealed by quantitative profiling," Nature Communications, Nature, vol. 6(1), pages 1-10, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms8440
    DOI: 10.1038/ncomms8440
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