Context influences on TALE-DNA binding revealed by quantitative profiling

• Main Authors: Rogers, Julia M. (Author), Reyon, Deepak (Author), Sander, Jeffry D. (Author), Kellis, Manolis (Contributor), Joung, J. Keith (Author), Bulyk, Martha L. (Contributor), Barrera, Luis Alberto (Contributor)
• Other Authors: Harvard University- (Contributor), Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Nature Publishing Group, 2015-09-14T13:58:01Z.
• Subjects: Article
• Online Access: Get fulltext

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.
National Science Foundation (U.S.). Graduate Research Fellowship
National Human Genome Research Institute (U.S.) (Grant R21 HG007573)