In Crystallo Selection to Establish New RNA Crystal Contacts.

Crystallography is a major technique for determining large RNA structures. Obtaining diffraction-quality crystals has been the bottleneck. Although several RNA crystallization methods have been developed, the field strongly needs additional approaches. Here we invented an in crystallo selection strategy for identifying mutations that enhance a target RNA's crystallizability. The strategy includes constructing an RNA pool containing random mutations, obtaining crystals, and amplifying the sequences enriched by crystallization. We demonstrated a proof-of-principle application to the P4-P6 domain from the Tetrahymena ribozyme. We further determined the structures of four selected mutants. All four establish new crystal lattice contacts while maintaining the native structure. Three mutants achieve this by relocating bulges and one by making a helix more flexible. In crystallo selection provides opportunities to improve crystals of RNAs or RNA-ligand complexes. Our results also suggest that mutants may be rationally designed for crystallization by "walking" a bulge along the RNA chain.

Heme promotes transcriptional and demethylase activities of Gis1, a member of the histone demethylase JMJD2/KDM4 family.

The yeast Gis1 protein is a transcriptional regulator belonging to the JMJD2/KDM4 subfamily of demethylases that contain a JmjC domain, which are highly conserved from yeast to humans. They have important functions in histone methylation, cellular signaling and tumorigenesis. Besides serving as a cofactor in many proteins, heme is known to directly regulate the activities of proteins ranging from transcriptional regulators to potassium channels. Here, we report a novel mechanism governing heme regulation of Gis1 transcriptional and histone demethylase activities. We found that two Gis1 modules, the JmjN + JmjC domain and the zinc finger (ZnF), can bind to heme specifically in vitro. In vivo functional analysis showed that the ZnF, not the JmjN + JmjC domain, promotes heme activation of transcriptional activity. Likewise, measurements of the demethylase activity of purified Gis1 proteins showed that full-length Gis1 and the JmjN + JmjC domain both possess demethylase activity. However, heme potentiates the demethylase activity of full-length Gis1, but not that of the JmjN + JmjC domain, which can confer heme activation of transcriptional activity in an unrelated protein. These results demonstrate that Gis1 represents a novel class of multi-functional heme sensing and signaling proteins, and that heme binding to the ZnF stimulates Gis1 demethylase and transcriptional activities.

The DGCR8 RNA-binding heme domain recognizes primary microRNAs by clamping the hairpin.

Canonical primary microRNA transcripts (pri-miRNAs) are characterized by a ∼30 bp hairpin flanked by single-stranded regions. These pri-miRNAs are recognized and cleaved by the Microprocessor complex consisting of the Drosha nuclease and its obligate RNA-binding partner DGCR8. It is not well understood how the Microprocessor specifically recognizes pri-miRNA substrates. Here, we show that in addition to the well-known double-stranded RNA-binding domains, DGCR8 uses a dimeric heme-binding domain to directly contact pri-miRNAs. This RNA-binding heme domain (Rhed) directs two DGCR8 dimers to bind each pri-miRNA hairpin. The two Rhed-binding sites are located at both ends of the hairpin. The Rhed and its RNA-binding surface are important for pri-miRNA processing activity. Additionally, the heme cofactor is required for formation of processing-competent DGCR8-pri-miRNA complexes. Our study reveals a unique protein-RNA interaction central to pri-miRNA recognition. We propose a unifying model in which two DGCR8 dimers clamp a pri-miRNA hairpin using their Rheds.

Ortholog identification in the presence of domain architecture rearrangement.

Ortholog identification is used in gene functional annotation, species phylogeny estimation, phylogenetic profile construction and many other analyses. Bioinformatics methods for ortholog identification are commonly based on pairwise protein sequence comparisons between whole genomes. Phylogenetic methods of ortholog identification have also been developed; these methods can be applied to protein data sets sharing a common domain architecture or which share a single functional domain but differ outside this region of homology. While promiscuous domains represent a challenge to all orthology prediction methods, overall structural similarity is highly correlated with proximity in a phylogenetic tree, conferring a degree of robustness to phylogenetic methods. In this article, we review the issues involved in orthology prediction when data sets include sequences with structurally heterogeneous domain architectures, with particular attention to automated methods designed for high-throughput application, and present a case study to illustrate the challenges in this area.