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

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.

SUBMITTER: Rogers JM

PROVIDER: S-EPMC4467457 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Context Influences on TALE-DNA Binding Revealed by Quantitative Profiling

Project description:Transcription Activator Like Effector (TALE) proteins have been described as recognizing DNA using a simple DNA-binding code, which has allowed for their use in many techniques requiring precise targeting of genomic loci. The TALE DNA binding domain is composed of an array of short repeats that differ in two key DNA-contacting positions, the repeat variable diresidues (RVD); the identities of the amino acids at these positions determine which base the repeat binds. While this simple code has been used successfully to design TALEs to target specific sequences, off-target binding has also been observed, indicating the need for a more rigorous understanding of the factors that determine TALE specificity. In this study, we assayed the DNA binding specificities of 21 TALE proteins of different lengths and RVD compositions using custom-designed Protein Binding Microarrays (PBMs). Position weight matrices derived from these data were used to develop a model to predict the specificity of any TALE protein. This modeling shows the large influence of protein length, RVD position, and neighboring RVD identity on binding specificity, indicating that the simple cipher-like code does not fully capture the complexity of TALE-DNA binding.

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