Indiana University-Purdue University Indianapolis (IUPUI) / Non-coding RNAs across the genome have been associated with various biological processes, ranging from regulation of splicing to remodeling of chromatin. Amongst the repertoire of non-coding sequences lies a critical species of RNAs called long non-coding RNAs (lncRNAs). LncRNAs significantly contribute to a large spectrum of human phenotypes, including cancers, Heart failure, Diabetes, and Alzheimer’s disease. This dissertation emphasizes the need to characterize the functional role of lncRNAs to improve our understanding of human diseases. This work consolidates a resource from multiple computational genomics and natural language processing-based approaches to advance our ability to functionally annotate hundreds of lncRNAs and their interactions, providing a one-stop lncRNA functional annotation and dynamic interaction network and multi-facet omics data visualization platform. RNA interactions are vital in various cellular processes, from transcription to RNA processing. These interactions dictate the functional scope of the RNA. However, the multifaceted functional nature of RNA stems from its ability to form secondary structures. Therefore, this work establishes a computational method to characterize RNA secondary structure by integrating SHAPE-seq and long-read sequencing to enhance further our understanding of RNA structure in modulating the post-transcriptional regulatory processes and deciphering the influence at several layers of biological features, ranging from structure composition to consequent protein occupancy. This study will potentially impact the research community by providing methods, web interfaces, and computational pipelines, improving our functional understanding of long non-coding RNAs. This work also provides novel integration methods of technologies like Oxford Nanopore-based long-read sequencing, RNA structure-probing methods, and machine learning. The approaches developed in this dissertation are scalable and adaptable to investigate further the functional and regulatory role of RNA and its structure. Overall, this study accelerates the development of RNA-based diagnostics and the identification of therapeutic targets in human disease.

Detection and analysis of protein-ligand binding sites is an important area of research in drug discovery. The FTMap web server is an established computational method for detection of binding hot spots, or regions on the protein surface that contribute disproportionately to the ligand binding free energy. This body of work primarily focuses on the utilization and advancement of FTMap for the study of protein-ligand interactions and their applications to drug discovery. First, the driving forces behind why some proteins require compounds beyond Lipinski’s rule-of-five (bRo5) guidelines are evaluated for 37 protein targets. Three types of proteins are identified on the basis of their binding hot spots, described by FTMap, and their ligand binding affinity profiles. We describe the multifaceted motivations for bRo5 drug discovery for each group of targets, including increased binding affinity, improved selectivity, decreased toxicity, and decreased off-target effects. Second, the conservation of surface binding properties in protein models is evaluated, with particular emphasis on their utility in drug discovery. Here, the probe-binding locations determined by FTMap are used to generate a binding fingerprint, and the Pearson correlation between the binding fingerprint of an experimental structure and a predicted model indicates the level of surface property conservation, without any knowledge of the protein function a priori. This analysis was performed on the protein models submitted to the Critical Assessment of Techniques for Protein Structure Prediction (CASP) rounds 12 and 14, and results were correlated with well-established structure quality metrics. Third, development of the publicly-available FTMove web server (https://ftmove.bu.edu) is described for detection of binding sites and their respective strengths across multiple different conformations of a protein. FTMove was tested on 22 proteins with known allosteric binding sites, and reliably identified both the orthosteric and allosteric binding sites as highly ranked binding sites. The results yield important insight into the dynamics and druggability of such binding sites. Finally, high throughput affinity purified, mass spectrometry data is evaluated for identification of protein-metabolite interactions (PMIs) in Escherichia coli. A detailed search for known PMIs in both the Protein Data Bank and KEGG database is described, and the resulting curated sets of 21 recovered and 37 potentially novel PMIs in E. Coli are presented. Finally, high confidence novel PMIs were evaluated with the template-based small molecule docking program, LigTBM. / 2023-01-26T00:00:00Z

Yes / The creation of co-crystals as a route to creating new pharmaceutical phases with modified or defined physicochemical properties is an area of intense research. Much of the current research has focused on creating new phases for numerous active pharmaceutical ingredients (APIs) to alter physical properties such as low solubilities, enhancing processability or stability. Such studies have identified suitable co-formers and common bonding motifs to aid with the design of new co-crystals but understanding how the changes in the molecular structure of the components are reflected in the packing and resulting properties is still lacking. This lack of insight means that the design and growth of new co-crystals is still a largely empirical process with co-formers selected and then attempts to grow the different materials undertaken to evaluate the resulting properties. This work will report on the results of a combination of crystal structure database analysis with computational chemistry studies to identify what structural features are retained across a selection of families of co-crystals with common components. The competition between different potential hydrogen bonding motifs was evaluated using ab initio quantum mechanical calculations and this was related to the commonality in the packing motifs when observed. It is found while the stronger local bonding motifs are often retained within systems, the balance of weaker long-range packing forces gives rise to many subtle shifts in packing leading to greater challenges in the prediction of final crystal structures.

No / A crystal structure prediction (CSP) study of three small, rigid and structurally related organic compounds (differing only in the position and number of methyl groups) is presented. A tailor-made force field (TMFF; a non-transferable force field specific for each molecule) was constructed with the aid of a dispersion-corrected density functional theory method (the hybrid method). Parameters for all energy terms in each TMFF were fitted to reference data generated by the hybrid method. Each force field was then employed during structure generation. The experimentally observed crystal structures of two of the three molecules were found as the most stable crystal packings in the lists of their force-field-optimised structures. A number of the most stable crystal structures were re-optimised with the hybrid method. One experimental crystal structure was still calculated to be the most stable structure, whereas for another compound the experimental structure became the third most stable structure according to the hybrid method. For the third molecule, the experimentally observed polymorph, which was found to be the fourth most stable form using its TMFF, became the second most stable form. Good geometrical agreements were observed between the experimental structures and those calculated by both methods. The average structural deviation achieved by the TMFFs was almost twice that obtained with the hybrid method. The TMFF approach was extended by exploring the accuracy of a more general TMFF (GTMFF), which involved fitting the force-field parameters to the reference data for all three molecules simultaneously. This GTMFF was slightly less accurate than the individual TMFFs but still of sufficient accuracy to be used in CSP. A study of the isostructural relationships between these molecules and their crystal lattices revealed a potential polymorph of one of the compounds that has not been observed experimentally and that may be accessible in a thorough polymorph screen, through seeding, or through the use of a suitable tailor-made additive.