Cys2-His2 zinc finger proteins (ZFPs) are the largest family of transcription factors in higher metazoans. They also represent the most diverse family with regards to the composition of their recognition sequences. Although there are a number of ZFPs with characterized DNA-binding preferences, the specificity of the vast majority of ZFPs is unknown and cannot be directly inferred by homology due to the diversity of recognition residues present within individual fingers. Given the large number of unique zinc fingers and assemblies present across eukaryotes, a comprehensive predictive recognition model that could accurately estimate the DNA-binding specificity of any ZFP based on its amino acid sequence would have great utility. Toward this goal, we have used the DNA-binding specificities of 678 two-finger modules from both natural and artificial sources to construct a random forest-based predictive model for ZFP recognition. We find that our recognition model outperforms previously described determinant-based recognition models for ZFPs, and can successfully estimate the specificity of naturally occurring ZFPs with previously defined specificities.

Cys(2)-His(2) zinc finger proteins (ZFPs) are the largest family of transcription factors in higher metazoans. They also represent the most diverse family with regards to the composition of their recognition sequences. Although there are a number of ZFPs with characterized DNA-binding preferences, the specificity of the vast majority of ZFPs is unknown and cannot be directly inferred by homology due to the diversity of recognition residues present within individual fingers. Given the large number of unique zinc fingers and assemblies present across eukaryotes, a comprehensive predictive recognition model that could accurately estimate the DNA-binding specificity of any ZFP based on its amino acid sequence would have great utility. Toward this goal, we have used the DNA-binding specificities of 678 two-finger modules from both natural and artificial sources to construct a random forest-based predictive model for ZFP recognition. We find that our recognition model outperforms previously described determinant-based recognition models for ZFPs, and can successfully estimate the specificity of naturally occurring ZFPs with previously defined specificities.

The C2H2 zinc finger is the most commonly utilized framework for engineering DNA-binding domains with novel specificities. Many different selection strategies have been developed to identify individual fingers that possess a particular DNA-binding specificity from a randomized library. In these experiments, each finger is selected in the context of a constant finger framework that ensures the identification of clones with a desired specificity by properly positioning the randomized finger on the DNA template. Following a successful selection, multiple zinc-finger clones are typically recovered that share similarities in the sequences of their DNA-recognition helices. In principle, each of the clones isolated from a selection is a candidate for assembly into a larger multi-finger protein, but to date a high-throughput method for identifying the most specific candidates for incorporation into a final multi-finger protein has not been available. Here we describe the development of a specificity profiling system that facilitates rapid and inexpensive characterization of engineered zinc-finger modules. Moreover, we demonstrate that specificity data collected using this system can be employed to rationally design zinc fingers with improved DNA-binding specificities.