7-Methylguanosine 5′ cap on mRNA is necessary for efficient protein expression in vitro and in vivo. Recent studies revealed structural diversity of endogenous mRNA caps, which carry different 5′-terminal nucleotides and additional methylations (2′-O-methylation and m6A). Currently available 5′-capping methods do not address this diversity. We report trinucleotide 5′ cap analogs (m7GpppN(m)pG), which are utilized by RNA polymerase T7 to initiate transcription from templates carrying Φ6.5 promoter and enable production of mRNAs differing in the identity of the first transcribed nucleotide (N = A, m6A, G, C, U) and its methylation status (±2′-O-methylation). HPLC-purified mRNAs carrying these 5′ caps were used to study protein expression in three mammalian cell lines (3T3-L1, HeLa and JAWS II). The highest expression was observed for mRNAs carrying 5′-terminal A/Am and m6Am, whereas the lowest was observed for G and Gm. The mRNAs carrying 2′-O-methyl at the first transcribed nucleotide (cap 1) had significantly higher expression than unmethylated counterparts (cap 0) only in JAWS II dendritic cells. Further experiments indicated that the mRNA expression characteristic does not correlate with affinity for translation initiation factor 4E or in vitro susceptibility to decapping, but instead depends on mRNA purity and the immune state of the cells.
RNAComposer is a fully automated, web-interfaced system for RNA 3D structure prediction, freely available at http://rnacomposer.cs.put.poznan.pl/ and http://rnacomposer.ibch.poznan.pl/. Its main components are: manually curated database of RNA 3D structure elements, highly efficient computational engine and user-friendly web application. In this paper, we demonstrate how the latest additions to the system allow the user to significantly affect the process of 3D model composition on several computational levels. Although in general our method is based on the knowledge of secondary structure topology, currently the RNAComposer offers a choice of six incorporated programs for secondary structure prediction. It also allows to apply a conditional search in the database of 3D structure elements and introduce user-provided elements into the final 3D model. This new functionality contributes to a significant improvement of the predicted 3D model reliability and it facilitates a better model adjustment to the experimental data. This is exemplified based on the RNAComposer application for modelling of the 3D structures of precursors of the miR160 family members.
The continuously increasing amount of RNA sequence and experimentally determined 3D structure data drives the development of computational methods supporting exploration of these data. Contemporary functional analysis of RNA molecules, such as ribozymes or riboswitches, covers various issues, among which tertiary structure modeling becomes more and more important. A growing number of tools to model and predict RNA structure calls for an evaluation of these tools and the quality of outcomes their produce. Thus, the development of reliable methods designed to meet this need is relevant in the context of RNA tertiary structure analysis and can highly influence the quality and usefulness of RNA tertiary structure prediction in the nearest future. Here, we present RNAlyzer—a computational method for comparison of RNA 3D models with the reference structure and for discrimination between the correct and incorrect models. Our approach is based on the idea of local neighborhood, defined as a set of atoms included in the sphere centered around a user-defined atom. A unique feature of the RNAlyzer is the simultaneous visualization of the model-reference structure distance at different levels of detail, from the individual residues to the entire molecules.
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