<b>Applications RNA Structure prediction and analysis</b><P> <b>HDV</b>Our massively parallel genetic algorithm for RNA structure prediction (MPGAfold) and StructureLab, our RNA structure analysis workbench, were applied in a study of the Hepatitis Delta virus (HDV). HDV is a virus associated with the Hepatitis B virus (HBV). It replicates in the presence of HBV. HDV with HBV increases the severity of liver disease and enhances the likelihood of developing liver cancer. HDV produces one protein, the hepatitis delta antigen, which has two forms, the short and the long form. Previously we showed, with the use of MPGAfold, that the Ecuadorian strain (ES) of HDV attains two secondary structures that are crucial for functionality. The HDV RNA is edited when it attains a branched conformation, changing a stop codon into a tryptophan. Later, the virus changes into a linear form which is necessary for replication, leading to the translation of a longer peptide which inhibits viral synthesis. At times the RNA bypasses the branched form and attains the linear replication form, avoiding editing, resulting in a shorter peptide required for HDV replication. Recently, MPGAfold indicated that the Peruvian strain (PS) of HDV had different folding characteristics than ES. ES attained the editing structure more readily. Our collaborator John Casey verified this with experiments and showed that ES binds to its editing protein adenosine deaminase less efficiently than PS. These results showed that HDV strains maintain a delicate balance between the formation of the editing and replication states. <P> <b>eIF4E</b>In collaboration with Dr. Nancy Colburn from NCI-CCR we characterized the molecular properties of mRNAs that are regulated by the oncogenic translation factor eIF4E. Cap-dependent translation in eukaryotes is mediated by the initiation factor eIF4E, whose presence is required for ribosomal recruitment. eIF4E is often over-expressed in human cancer and targeting eIF4E in animal models has shown positive therapeutic effects. Activation of eIF4E induces increased translation of specific mRNAs that encode oncoproteins. This activation can be due to tumor promoting signals originating from AKT signaling. A question arises as to what characteristics enable over-responsiveness to eIF4E. To establish these features, we analyzed two datasets, one from cells over-expressing the eIF4E protein and the other from cells activated by the oncogenic AKT pathway. By comparing mRNAs that shift from monosomes into actively translating polyribosome fractions as a result of eIF4E overexpression, with mRNAs that do not shift, one can determine those transcripts that are up-regulated, down-regulated or remain about the same after increased eIF4E exposure. Our computational analysis showed that up-regulated mRNAs have on average shorter 3'UTRs, higher G+C content and slightly more RNA secondary structure before the start codon and around the stop codon. There is also apparent diminution of binding sites for microRNAs that are known to be tumor suppressors for mRNAs that are highly responsive to increased eIF4E concentration. We also designed a classifier based on G+C content, 3'UTR length and total length that is capable of predicting polysomal movement in the provided datasets. <P><b>TCV</b>3' UTRs of cellular and viral mRNAs harbor elements that function in gene expression by enhancing translation using as unknown mechanisms. To determine the function of these elements we used a simple model virus, Turnip crinkle virus (TCV). TCV is translated in a cap-independent fashion and contains a 3'proximal region that together with the 5'UTR synergistically enhances translation. In collaboration with Professor Anne Simon, from the University of Maryland, we are deciphering the function of this 3'element. We used MPGAfold and Structurelab to identify a series of hairpins and one pseudoknot that have been confirmed genetically. Using this RNA secondary structural information with RNA2D3D, our molecular modeling software for RNA 3D structure determination, we predicted that a series of three hairpins and two pseudoknots structurally resembled a tRNA, the first internal tRNA-like structure found in nature. Using this information, we proposed that translational enhancement by the element might involve ribosome binding. The element was found to bind ribosomes by interaction with the 60S ribosomal subunit, the first such interaction with the large subunit discovered. It was biochemically determined that this tRNA-like element is a major part of a structural switch that converts the template from one that is translated to one that is replicated. <P><b>Musashi</b>Musashi1 (Msi1) is a highly conserved RNA binding protein with functions in stem cell maintenance and development of the nervous system. There is good evidence that links Msi1 to tumor formation. A high-throughput approach is being used by our collaborator, Dr. Luiz Penalva at the University of Texas Health Science Center to identify a group of target mRNAs and to elucidate their participation in stem cell maintenance, cell differentiation and tumorigenesis. mRNAs preferentially associated with Msi1 were identified. Our group applied computational data mining to find the regulatory signal and structural motif in the 3'UTR of Msi1 targeted genes. We developed two models for the binding sequence of Msi1 in the 3'UTR of these mRNAs regulated by Msi1. In both models distinct RNA structures which are highly stable and statistically significant were found with our program EDscan. These were experimentally confirmed to interact with the Neural RNA-binding protein Msi1. It thus appears that the regulation of the RNA-binding protein Msi1 is closely correlated with a conserved binding sequence and a structureal signal that is indicated in the two models. <P><b>RNA Structure Prediction and Analysis Software:</b><P><b>Pseudo energy minimization</b>Simulation algorithms that are based on thermodynamic processes often minimize the free energy of folding of single RNA sequences to predict their secondary structures. The additional use of covariance scores derived from multiple sequence alignments can improve the accuracy of these predictions. We developed with Jason Wang at the New Jersey Institute of Technology, an algorithm, RSpredict, that predicts the consensus secondary structure of a set of aligned sequences that combines the principles of dynamic programming with covariation scores. We can predict fairly accurately a consensus secondary structure from the set of aligned sequences. <P> <b>Combining NMR and SAXS</b>The determination of large 3D RNA structures by NMR, X-ray crystallography or other experimental techniques has been a very difficult problem. Our group with Yun-Xing Wang's group in CCR, has developed a methodology that combines techniques from Nuclear Magnetic Resonance (NMR) and Small Angle X-ray scattering with a software package called G2G to determine the global architecture of large RNAs consisting mostly of A-form helices. A-form-like helices comprise a large percentage of the structures determined by X-ray crystallography thus they are a predominant building block for RNA structure. Therefore the determination of the orientation and the rotation of helices around their helical axes and the relative global positions of the he [summary truncated at 7800 characters]
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