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Nat Commun. 2015 Jun 11;6:7440. doi: 10.1038/ncomms8440.

Context influences on TALE-DNA binding revealed by quantitative profiling.

Author information

1
1] Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA [2] Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, Massachusetts 02138, USA.
2
1] Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA [2] Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, Massachusetts 02138, USA [3] Harvard-MIT Division of Health Sciences and Technology (HST), Harvard Medical School, Boston, Massachusetts 02115, USA [4] Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts 02139, USA.
3
1] Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA [2] Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA [3] Center for Cancer Research, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA [4] Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA.
4
Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts 02139, USA.
5
1] Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA [2] Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, Massachusetts 02138, USA [3] Harvard-MIT Division of Health Sciences and Technology (HST), Harvard Medical School, Boston, Massachusetts 02115, USA [4] Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.

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.

PMID:
26067805
PMCID:
PMC4467457
DOI:
10.1038/ncomms8440
[Indexed for MEDLINE]
Free PMC Article

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