Person:

Bulyk, Martha

Motivation: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity. New techniques that estimate the affinity of TFs to all possible (\kappa)-mers provide a new opportunity to study DNA–protein interaction mechanisms, and may facilitate inference of binding preferences for members of a given TF family when such information is available for other family members. Results: We employed a new dataset consisting of the relative preferences of mouse homeodomains for all eight-base DNA sequences in order to ask how well we can predict the binding profiles of homeodomains when only their protein sequences are given. We evaluated a panel of standard statistical inference techniques, as well as variations of the protein features considered. Nearest neighbour among functionally important residues emerged among the most effective methods. Our results underscore the complexity of TF–DNA recognition, and suggest a rational approach for future analyses of TF families.

Members of the large ETS family of transcription factors (TFs) have highly similar DNA-binding domains (DBDs)—yet they have diverse functions and activities in physiology and oncogenesis. Some differences in DNA-binding preferences within this family have been described, but they have not been analysed systematically, and their contributions to targeting remain largely uncharacterized. We report here the DNA-binding profiles for all human and mouse ETS factors, which we generated using two different methods: a high-throughput microwell-based TF DNA-binding specificity assay, and protein-binding microarrays (PBMs). Both approaches reveal that the ETS-binding profiles cluster into four distinct classes, and that all ETS factors linked to cancer, ERG, ETV1, ETV4 and FLI1, fall into just one of these classes. We identify amino-acid residues that are critical for the differences in specificity between all the classes, and confirm the specificities in vivo using chromatin immunoprecipitation followed by sequencing (ChIP-seq) for a member of each class. The results indicate that even relatively small differences in in vitro binding specificity of a TF contribute to site selectivity in vivo.

Classifying proteins into subgroups with similar molecular function on the basis of sequence is an important step in deriving reliable functional annotations computationally. So far, however, available classification procedures have been evaluated against protein subgroups that are defined by experts using mainly qualitative descriptions of molecular function. Recently, in vitro DNA-binding preferences to all possible 8-nt DNA sequences have been measured for 178 mouse homeodomains using protein-binding microarrays, offering the unprecedented opportunity of evaluating the classification methods against quantitative measures of molecular function. To this end, we automatically derive homeodomain subtypes from the DNA-binding data and independently group the same domains using sequence information alone. We test five sequence-based methods, which use different sequence-similarity measures and algorithms to group sequences. Results show that methods that optimize the classification robustness reflect well the detailed functional specificity revealed by the experimental data. In some of these classifications, 73–83% of the subfamilies exactly correspond to, or are completely contained in, the function-based subtypes. Our findings demonstrate that certain sequence-based classifications are capable of yielding very specific molecular function annotations. The availability of quantitative descriptions of molecular function, such as DNA-binding data, will be a key factor in exploiting this potential in the future.