Misexpression of the double homeodomain transcription factor DUX4 results in facioscapulohumeral muscular dystrophy (FSHD). A DNA-binding consensus with two tandem TAAT motifs based on chromatin IP peaks has been discovered; however, the consensus has multiple variations (flavors) of unknown relative activity. In addition, not all peaks have this consensus, and the Pitx1 promoter, the first DUX4 target sequence mooted, has a different TAAT-rich sequence. Furthermore, it is not known whether and to what extent deviations from the consensus affect DNA-binding affinity and transcriptional activation potential. Here, we take both unbiased and consensus sequence-driven approaches to determine the DNA-binding specificity of DUX4 and its tolerance to mismatches at each site within its consensus sequence. We discover that the best binding and the greatest transcriptional activation are observed when the two TAAT motifs are separated by a C residue. The second TAAT motif in the consensus sequence is actually (T/C)AAT. We find that a T is preferred here. DUX4 has no transcriptional activity on “half-sites”, i.e., those bearing only a single TAAT motif. We further find that DUX4 does not bind to the TAATTA motif in the Pitx1 promoter, that Pitx1 sequences have no competitive band shift activity, and that the Pitx1 sequence is transcriptionally inactive, calling into question PITX1 as a DUX4 target gene. Finally, by multimerizing binding sites, we find that DUX4 transcriptional activation demonstrates tremendous synergy and that at low DNA concentrations, at least two motifs are necessary to detect a transcriptional response. These studies illuminate the DNA-binding sequence preferences of DUX4.

A new approach to the functional classification of protein 3D structures is described with application to some examples from structural genomics. This approach is based on functional site prediction with THEMATICS and POOL. THEMATICS employs calculated electrostatic potentials of the query structure. POOL is a machine learning method that utilizes THEMATICS features and has been shown to predict accurate, precise, highly localized interaction sites. Extension to the functional classification of structural genomics proteins is now described. Predicted functionally important residues are structurally aligned with those of proteins with previously characterized biochemical functions. A 3D structure match at the predicted local functional site then serves as a more reliable predictor of biochemical function than an overall structure match. Annotation is confirmed for a structural genomics protein with the ribulose phosphate binding barrel (RPBB) fold. A putative glucoamylase from Bacteroides fragilis (PDB ID 3eu8) is shown to be in fact probably not a glucoamylase. Finally a structural genomics protein from Streptomyces coelicolor annotated as an enoyl-CoA hydratase (PDB ID 3g64) is shown to be misannotated. Its predicted active site does not match the well-characterized enoyl-CoA hydratases of similar structure but rather bears closer resemblance to those of a dehalogenase with similar fold.

As a result of high‐throughput protein structure initiatives, over 14,400 protein structures have been solved by Structural Genomics (SG) centers and participating research groups. While the totality of SG data represents a tremendous contribution to genomics and structural biology, reliable functional information for these proteins is generally lacking. Better functional predictions for SG proteins will add substantial value to the structural information already obtained. Our method described herein, Graph Representation of Active Sites for Prediction of Function (GRASP‐Func), predicts quickly and accurately the biochemical function of proteins by representing residues at the predicted local active site as graphs rather than in Cartesian coordinates. We compare the GRASP‐Func method to our previously reported method, Structurally Aligned Local Sites of Activity (SALSA), using the Ribulose Phosphate Binding Barrel (RPBB), 6‐Hairpin Glycosidase (6‐HG), and Concanavalin A‐like Lectins/Glucanase (CAL/G) superfamilies as test cases. In each of the superfamilies, SALSA and the much faster method GRASP‐Func yield similar correct classification of previously characterized proteins, providing a validated benchmark for the new method. In addition, we analyzed SG proteins using our SALSA and GRASP‐Func methods to predict function. Forty‐one SG proteins in the RPBB superfamily, nine SG proteins in the 6‐HG superfamily, and one SG protein in the CAL/G superfamily were successfully classified into one of the functional families in their respective superfamily by both methods. This improved, faster, validated computational method can yield more reliable predictions of function that can be used for a wide variety of applications by the community.