Faculty Bibliography

The recognition potential of most families of DNA-binding domains (DBDs) remains relatively unexplored. Homeodomains (HDs), like many other families of DBDs, display limited diversity in their preferred recognition sequences. To explore the recognition potential of HDs, we utilized a bacterial selection system to isolate HD variants, from a randomized library, that are compatible with each of the 64 possible 3' triplet sites (i.e., TAANNN). The majority of these selections yielded sets of HDs with overrepresented residues at specific recognition positions, implying the selection of specific binders. The DNA-binding specificity of 151 representative HD variants was subsequently characterized, identifying HDs that preferentially recognize 44 of these target sites. Many of these variants contain novel combinations of specificity determinants that are uncommon or absent in extant HDs. These novel determinants, when grafted into different HD backbones, produce a corresponding alteration in specificity. This information was used to create more explicit HD recognition models, which can inform the prediction of transcriptional regulatory networks for extant HDs or the engineering of HDs with novel DNA-recognition potential. The diversity of recovered HD recognition sequences raises important questions about the fitness barrier that restricts the evolution of alternate recognition modalities in natural systems.

MOTIVATION: Recognition models for protein-DNA interactions, which allow the prediction of specificity for a DNA-binding domain based only on its sequence or the alteration of specificity through rational design, have long been a goal of computational biology. There has been some progress in constructing useful models, especially for C(2)H(2) zinc finger proteins, but it remains a challenging problem with ample room for improvement. For most families of transcription factors the best available methods utilize k-nearest neighbor (KNN) algorithms to make specificity predictions based on the average of the specificities of the k most similar proteins with defined specificities. Homeodomain (HD) proteins are the second most abundant family of transcription factors, after zinc fingers, in most metazoan genomes, and as a consequence an effective recognition model for this family would facilitate predictive models of many transcriptional regulatory networks within these genomes. RESULTS: Using extensive experimental data, we have tested several machine learning approaches and find that both support vector machines and random forests (RFs) can produce recognition models for HD proteins that are significant improvements over KNN-based methods. Cross-validation analyses show that the resulting models are capable of predicting specificities with high accuracy. We have produced a web-based prediction tool, PreMoTF (Predicted Motifs for Transcription Factors) (http://stormo.wustl.edu/PreMoTF), for predicting position frequency matrices from protein sequence using a RF-based model.

The DNA-binding specificity of transcription factors allows the prediction of regulatory targets in a genome. However, very few factor specificities have been characterized and still too little is known about how these proteins interact with their targets to make predictions a priori. To provide a greater understanding of how proteins and DNA interact, we have developed a bacterial one-hybrid system that allows the sensitive, high-throughput, and cost-effective assay of the interaction at the protein-DNA interface. This system makes survival of the bacteria dependent on activation of the reporter gene and therefore dependent on the protein-DNA interaction that recruits the polymerase. We have used this system to characterize DNA-binding specificities for representative members of the most common DNA-binding domain (DBD) families. We have also been able to engineer DBDs with novel specificity to be used as artificial transcription factors and zinc finger nucleases. The B1H assay provides a simple and inexpensive method to investigate protein-DNA interactions that is accessible to essentially any laboratory.

We describe the comprehensive characterization of homeodomain DNA-binding specificities from a metazoan genome. The analysis of all 84 independent homeodomains from D. melanogaster reveals the breadth of DNA sequences that can be specified by this recognition motif. The majority of these factors can be organized into 11 different specificity groups, where the preferred recognition sequence between these groups can differ at up to four of the six core recognition positions. Analysis of the recognition motifs within these groups led to a catalog of common specificity determinants that may cooperate or compete to define the binding site preference. With these recognition principles, a homeodomain can be reengineered to create factors where its specificity is altered at the majority of recognition positions. This resource also allows prediction of homeodomain specificities from other organisms, which is demonstrated by the prediction and analysis of human homeodomain specificities.

Direct genomic manipulation at a specific locus is still not feasible in most vertebrate model organisms. In vertebrate cell lines, genomic lesions at a specific site have been introduced using zinc-finger nucleases (ZFNs). Here we adapt this technology to create targeted mutations in the zebrafish germ line. ZFNs were engineered that recognize sequences in the zebrafish ortholog of the vascular endothelial growth factor-2 receptor, kdr (also known as kdra). Co-injection of mRNAs encoding these ZFNs into one-cell-stage zebrafish embryos led to mutagenic lesions at the target site that were transmitted through the germ line with high frequency. The use of engineered ZFNs to introduce heritable mutations into a genome obviates the need for embryonic stem cell lines and should be applicable to most animal species for which early-stage embryos are easily accessible.

Specificity data for groups of transcription factors (TFs) in a common regulatory network can be used to computationally identify the location of cis-regulatory modules in a genome. The primary limitation for this type of analysis is the paucity of specificity data that is available for the majority of TFs. We describe an omega-based bacterial one-hybrid system that provides a rapid method for characterizing DNA-binding specificities on a genome-wide scale. Using this system, 35 members of the Drosophila melanogaster segmentation network have been characterized, including representative members of all of the major classes of DNA-binding domains. A suite of web-based tools was created that uses this binding site dataset and phylogenetic comparisons to identify cis-regulatory modules throughout the fly genome. These tools allow specificities for any combination of factors to be used to perform rapid local or genome-wide searches for cis-regulatory modules. The utility of these factor specificities and tools is demonstrated on the well-characterized segmentation network. By incorporating specificity data on an additional 66 factors that we have characterized, our tools utilize approximately 14% of the predicted factors within the fly genome and provide an important new community resource for the identification of cis-regulatory modules.