FOXA2, a member of the forkhead family of transcription factors, plays essential roles in liver development and bile acid homeostasis. In this study, we report a 2.8 Å co-crystal structure of the FOXA2 DNA-binding domain (FOXA2-DBD) bound to a DNA duplex containing a forkhead consensus binding site (GTAAACA). The FOXA2-DBD adopts the canonical winged-helix fold, with helix H3 and wing 1 regions mainly mediating the DNA recognition. Although the wing 2 region was not defined in the structure, isothermal titration calorimetry assays suggested that this region was required for optimal DNA binding. Structure comparison with the FOXA3-DBD bound to DNA revealed more major groove contacts and fewer minor groove contacts in the FOXA2 structure than in the FOXA3 structure. Structure comparison with the FOXO1-DBD bound to DNA showed that different forkhead proteins could induce different DNA conformations upon binding to identical DNA sequences. Our findings provide the structural basis for FOXA2 protein binding to a consensus forkhead site and elucidate how members of the forkhead protein family bind different DNA sites.

DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory processes. Underlying mechanisms that explain the highly specific binding of TFs to their genomic target sites are poorly understood. A better understanding of TF−DNA binding requires the ability to quantitatively model TF binding to accessible DNA as its basic step, before additional in vivo components can be considered. Traditionally, these models were built based on nucleotide sequence. Here, we integrated 3D DNA shape information derived with a high-throughput approach into the modeling of TF binding specificities. Using support vector regression, we trained quantitative models of TF binding specificity based on protein binding microarray (PBM) data for 68 mammalian TFs. The evaluation of our models included cross-validation on specific PBM array designs, testing across different PBM array designs, and using PBM-trained models to predict relative binding affinities derived from in vitro selection combined with deep sequencing (SELEX-seq). Our results showed that shape-augmented models compared favorably to sequence-based models. Although both k-mer and DNA shape features can encode interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space. In addition, analyzing the feature weights of DNA shape-augmented models uncovered TF family-specific structural readout mechanisms that were not revealed by the DNA sequence. As such, this work combines knowledge from structural biology and genomics, and suggests a new path toward understanding TF binding and genome function.

DNA binding proteins find their cognate sequences within genomic DNA through recognition of specific chemical and structural features. Here we demonstrate that high-resolution DNase I cleavage profiles can provide detailed information about the shape and chemical modification status of genomic DNA. Analyzing millions of DNA backbone hydrolysis events on naked genomic DNA, we show that the intrinsic rate of cleavage by DNase I closely tracks the width of the minor groove. Integration of these DNase I cleavage data with bisulfite sequencing data for the same cell type’s genome reveals that cleavage directly adjacent to cytosine-phosphate-guanine (CpG) dinucleotides is enhanced at least eightfold by cytosine methylation. This phenomenon we show to be attributable to methylation-induced narrowing of the minor groove. Furthermore, we demonstrate that it enables simultaneous mapping of DNase I hypersensitivity and regional DNA methylation levels using dense in vivo cleavage data. Taken together, our results suggest a general mechanism by which CpG methylation can modulate protein–DNA interaction strength via the remodeling of DNA shape.