Computational Approaches for RNA Structure and Function Determination

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In collaboration with Shuo Gu we studied the nuanced functionalities of Drosha in cellular systems due to its importance for understanding the processing of microRNAs and how they relate to normal cellular activity as well as diseases such as cancer. Here we studied Drosha targeted stem-loop structures and the types of microRNA isoforms that were produced by these Drosha/RNA interactions. Experimental and computational approaches were applied to determine how the produced isoforms varied as a function of the RNA sequence and structure. Results indicate that bent, distorted and/or flexible structures in the targeted Drosha stem seem to facilitate the production of alternate forms of microRNAs. Structural predictions and experimental results were compared and correlated. Specifically, cleavage of pri-miR-9-1, but not pri-miR-9-2 or pri-miR-9-3, generated an alternative miR-9 with a shifted seed sequence that exapands the scope of its target RNAs. Interestingly, analysis of low-grade glioma patient samples indicate that alternative pri-miR-9 has a potential for tumor progression.Other pri-miRs were also studied and they also produced isoforms as a function of the targeted RNA's shape and flexibility --- In cell SHAPE prediction provides a new level of detail for determining RNA structure within cells. These results may vary from the more standard SHAPE techniques that do not take the cellular environment into account when producing potential structural predictions. We developed a method for the computational prediction of in cell SHAPE by training a neural network (which was optimized by hyper-paramaterization techniques) based on known in cell SHAPE measurements obtained from an E. coli database. Predictions, given a sequence, produce reasonably accurate results with a Pearson coefficient with experimental shape scores better than thermodynamic folding. As an example, we predicted the SHAPE scores around translation start sites in mRNAs. The method indicates that nucleotides immediately upstream of the translation start sites to be relatively unstructured. These results were found to be statistically significant, while in contrast, results based on thermodynamic folding were not. This is the first time that computational methods have been applied to the prediction of RNA structure within cells based on machine learning. ---In another project in collaboration with Mikhail Kashlev we determining motifs that during transcription are responsible for transcriptional termination. These motifs appear to go beyond the standard RNA hairpin that is normally involved in termination. The approach involves the use of MPGAFOLD, a massively parallel genetic algorithm the includes capabilities to predict RNA secondary structures that form during transcription, i.e. co-transcriptional folding as the RNA strand elongates. As it does local structures form. These structures in turn have the ability to form tertiary interactions which can influence the formation of termination motifs. These sequential secondary structure motifs are also modeled in 3D further verifying their potential formation and tertiary influence. A new paradigm for termination control may be indicated by these results.---Another project in collaboration with Stuart Le Grice involves the development of a computational approach to determined binding sites and affinities of small molecules targeting various RNA structural motifs. The goal of this project is to aid in the screening of small molecules for their potential to be therapeutically beneficial in targeting viral RNAs or cancer causing genes. The small molecules are initially derived from sets found by binding to experimental screening methods using small molecule microarrays. The pipeline as it currently stands is able to determine to a reasonable level of accuracy ligand poses as well as the conformation of the binding pockets. It also seem able to discriminate between different levels of binding affinities for different ligands. The pipe-line is currently being applied to the epsilon region of the hepatitis delta virus and to the triple stranded PAN. We are able to get good agreement with NMR and X-ray structure data respectively to these two significantly different sites. This methodology is opening the door to computational prediction of small molecule binding the RNA motifs for potential therapeutics purposes, a domain of research that has not been extensively explored.---In collaboration with Anne Simon, University of Maryland a new RNA structure drawing algorithm was developed, RNA2Drawer. RNA structure prediction programs remain imperfect and many substructures are still identified by manual exploration, which is most efficiently conducted within an RNA structure drawing program. RNA2Drawer was developed to allow for graphical structure editing while maintaining the geometry of a drawing (e.g., ellipsoid loops, stems with evenly stacked base pairs) throughout structural changes and manual adjustments to the layout by the user. In addition, the program allows for annotations such as colouring and circling of bases and drawing of tertiary interactions (e.g., pseudoknots). RNA2Drawer can also draw commonly desired elements such as an optionally flattened outermost loop and assists structure editing by automatically highlighting complementary subsequences, which facilitates the discovery of potentially new and alternative pairings, particularly tertiary pairings over long-distances, which are biologically critical in the genomes of many RNA viruses. RNA2Drawer outputs drawings either as PNG files, or as PPTX and SVG files, such that every object of a drawing (e.g., bases, bonds) is an individual PPTX or SVG object, allowing for further manipulation in Microsoft PowerPoint or a vector graphics editor such as Adobe Illustrator. --Also in collaboration with Anne Simon, University of Maryland, we have been exploring the RNA motifs that are involved in alternative modes of translation in eukaryotic systems. Specifically we have been concentrating on those that do not contain 5' cap sites and lack a poly A tail, cap Independent translation elements (CITE, or PTE), which is not the normal mode of translation, but is a mechanism found in several RNA viruses. We have found elements, via computational 3D modeling and experimental verification such as site directed mutagenesis and SHAPE, that seem to be common for example, in Carmoviruses that stabilize structures beyond pseudoknot motifs that are conducive for translation factor binding and thus mimic 5' cap sites.