期刊
NATURE COMMUNICATIONS
卷 6, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/ncomms8440
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资金
- NHGRI NIH HHS [R21 HG007573, R01 HG004037, T32 HG002295] Funding Source: Medline
- NIGMS NIH HHS [R01 GM113708, DP1 GM105378] Funding Source: Medline
- NCCDPHP CDC HHS [DP1 GM105378] Funding Source: Medline
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 similar 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.
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