CENTROIDFOLD: a web server for RNA secondary structure prediction

Computational approaches to 3D modeling of RNA

Many exciting discoveries have recently revealed the versatility of RNA and its importance in a variety of functions within the cell. Since the structural features of RNA are of major importance to their biological function, there is much interest in predicting RNA structure, either in free form or in interaction with various ligands, including proteins, metabolites and other molecules. In recent years, an increasing number of researchers have developed novel RNA algorithms for predicting RNA secondary and tertiary structures. In this review, we describe current experimental and computational advances and discuss recent ideas that are transforming the traditional view of RNA folding. To evaluate the performance of the most recent RNA 3D folding algorithms, we provide a comparative study in order to test the performance of available 3D structure prediction algorithms for an RNA data set of 43 structures of various lengths and motifs. We find that the algorithms vary widely in terms of prediction quality across different RNA lengths and topologies; most predictions have very large root mean square deviations from the experimental structure. We conclude by outlining some suggestions for future RNA folding research.

Vasa promotes Drosophila germline stem cell differentiation by activating mei-P26 translation by directly interacting with a (U)-rich motif in its 3' UTR

Vasa (Vas) is a DEAD-box RNA-binding protein required in Drosophila at several steps of oogenesis and for primordial germ cell (PGC) specification. Vas associates with eukaryotic initiation factor 5B (eIF5B), and this interaction has been implicated in translational activation of gurken mRNA in the oocyte. Vas is expressed in all ovarian germline cells, and aspects of the vas-null phenotype suggest a function in regulating the balance between germline stem cells (GSCs) and their fate-restricted descendants. We used a biochemical approach to recover Vas-associated mRNAs and obtained mei-P26, whose product represses microRNA activity and promotes GSC differentiation. We found that vas and mei-P26 mutants interact, and that mei-P26 translation is substantially reduced in vas mutant cells. In vitro, Vas protein bound specifically to a (U)-rich motif in the mei-P26 3' untranslated region (UTR), and Vas-dependent regulation of GFP-mei-P26 transgenes in vivo was dependent on the same (U)-rich 3' UTR domain. The ability of Vas to activate mei-P26 expression in vivo was abrogated by a mutation that greatly reduces its interaction with eIF5B. Taken together, our data support the conclusion that Vas promotes germ cell differentiation by directly activating mei-P26 translation in early-stage committed cells.