It is recognized that an iterative comparative analysis of large-scale homologous RNAs significantly promote the understanding of an RNA family. The Gutell lab is renowned for maintaining high quality RNA sequence alignments and accurately predicted RNA secondary structures using this approach. While the current available alignment and structure data are mainly obtained by trained domain experts with extensive manual effort, it is highly desired that this process is automated and replicable given the exponentially growing number of RNA sequence data and the amount of time required for expert training. In this thesis, we learn the processes involved in comparative analysis by manually aligning a non-coding RNA family, IVS sequences, with the supervision of Dr. Gutell. Each process is then simulated by mathematical objective functions and algorithms. We also evaluate the current available RNA analysis packages that aim each of the processes. Finally, a new RNA sequence alignment algorithm incorporating structure information that can be extended for different alignment tasks is proposed. / text

The research described in this dissertation is focused on the knowledge-based, often in silico assisted design, targeted synthesis, and biological evaluation of small molecules of interest for two translational medicinal chemistry projects. The first project (Part 1) is aimed at the identification of blood brain barrier (BBB) penetrable dual specificity tyrosine phosphorylation regulated kinase-1A (DYRK1A) inhibitors as a potential disease modifying approach to mitigate cognitive deficits associated with Alzheimer's neurodegeneration. Two major series with potent activity against DYRK1A were identified in addition to a number of other chemotype sub-series that also exhibit somewhat promising activity. Extensive profiling of active analogs revealed interesting biological activity and selectivity, which led to the identification of two analogs for in vivo studies and revealed new opportunities for further investigation into other kinase targets implicated in neurodegeneration and polypharmacological approaches. The second project (Part 2) is focused on the development of compounds that inhibit PGE₂ production, while not affecting cyclooxygenase (COX) activity, as a novel approach to treat cancer. Compounds were designed with the intention of inhibiting microsomal prostaglandin E₂ synthase-1 (mPGES-1); however, biological evaluation revealed phenotypically active compounds in a cell based assay with an unknown mechanism of action. Further profiling revealed promising anticancer activity in xenograft mouse models. In addition, PGE₂ has been implicated in an immune evasion mechanism of F. tularensis, a strain of bacteria that remains an exploitable threat in biowarfare, thus a small number of analogs were evaluated in a cell model of F. tularensis infection stimulated PGE₂ production.

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The pathogenic bacterium Mycobacterium tuberculosis (Mt) is the causative agent of tuberculosis, a widespread and fatal infectious disease. Today, treatment against tuberculosis involves a combination of drugs, which need to be taken for at least six months and which often causes severe side effects. Therefore, new drugs that are more effective and that give fewer side effects are needed. A characteristic feature of the Mt bacterium is its very complex and thick cell wall, which prevents many potential drug molecules from penetrating it. Inhibiting any one of the enzymes that are involved in its biosynthesis would therefore seem to be a good strategy for eliminating the Mt bacteria. The aim of this study was to characterize four enzymes involved in Mt cell wall biosynthesis. In order to do that, they were produced recombinantly in E. coli and purified. Crystallization experiments were set up in order to produce diffracting crystals, with the aim of structure determination and drug design.

Virtual Sreening (VS) is an in silico technique for drug discovery. An overview of VS methods is given and is seen to be approachable from two sides: structure based and ligand based. Structure based virtual screening uses explicit knowledge of the target receptor to suggest candidate receptor-ligand complexes. Ligand based virtual screening can infer required characteristics of binders from known ligands. A consideration for all virtual screening techniques is the amount of computing time required to arrive at a solution. For this reason, techniques of high performance computing have been applied to both the structural and ligand based approaches. A proven structure based virtual screening code LIDAEUS (Ligand Discovery At Edinburgh University) has been ported and parallelised to a massively parallel computing platform, the University of Edinburgh’s IBM Bluegene/l, consisting of 2,048 processor cores. A challenge in achieving scaling to such a large number of processors required implementation of a minimal communication parallel sort algorithm. Parallel efficiencies achieved within this parallelisation exceeded 99%, confirming that a near optimum strategy has been followed and capacity for running the code on a greater number of processors exists. This implementation of the program has been successfully used with a number of protein targets. The development of a new ligand based virtual screening code has been completed. The program UFSRAT (Ultra Fast Shape Recognition with Atom Types) takes the features of known binders and suggests molecules which will be able to make similar interactions. This similarity method is both fast (1 million molecules per hour per processor) and independent of input orientation. Along with UFSRAT, some other methods (VolRAT and UFSRGraph) based on UFSRAT have been developed, addressing different approaches to ligand based virtual screening. UFSRAT as an approach to discovering novel protein-ligand complexes has been validated with the discovery of a number of inhibitors for 11β-Hydroxysteroid Dehydrogenase type 1 and FK binding protein 12.

Flexible enzymes are notoriously a bane to structure-based drug design and discovery efforts. This is because no single structure can accurately capture the vast array of conformations that exist in solution and many are subject to ligand-associated structural changes that are difficult to predict. Glutamate racemase (GR) – an antibiotic drug discovery target involved in cell wall biosynthesis – is one such enzyme that has eluded basic structure-based drug design and discovery efforts due to these flexibility issues. In this study, our focus is on overcoming the impediment of unpredictable ligand-associated structural changes in GR drug discovery campaigns. The flexibility of the GR active site is such that it is capable of accommodating ligands with very different structures. Though these ligands may bind to the same pocket, they may associate with quite dissimilar conformations where some are more favorable for complexation than others. Knowledge of these changes is invaluable in guiding drug discovery efforts, indicating which compounds selectively associate with more favorable conformations and are therefore better suited for optimization and providing starting structures to guide structure-based drug design optimization efforts. In this study, we develop a mutant GR possessing a genetically encoded non-natural fluorescent amino acid in a region remote from the active site whose movement has been previously observed to correlate with active site changes. With this mutant GR, we observe a differential fluorescence pattern upon binding of two structurally distinct competitive inhibitors known to associate with unique GR conformations – one to a favorable conformation with a smaller, less solvated active site and the other to an unfavorable conformation with a larger, more solvated active site. A concomitant computational study ascribes the source of this differential fluorescence pattern to ligand-associated conformational changes resulting in changes to the local environment of the fluorescent residue. Therefore, this mutant permits the elucidation of valuable structural information with relative ease by simply monitoring the fluorescence pattern resulting from ligand binding, which indicates whether the ligand has bound to a favorable or unfavorable conformation and offers insight into the general structure of this conformation.

The objective of this study is to evaluate pseudo-substrates of pantothenate kinase (PanK) for the therapeutic treatment of multidrug resistant bacterial infections of Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Pantothenate (Pan) analogs, including N- pentylpantothenamide (N5-Pan) and N-heptylpantothenamide (N7-Pan), hamper bacterial growth by utilizing the PanK enzymes, which normally catalyze the rate determining step of the Coenzyme A biosynthetic pathway. Here we report the structures of SaPanK, Human PanK3 and EcPanK complexed with N7-Pan or N5-Pan, all of which have provided the opportunity to investigate the structural differences of bacterial and human Pan binding sites. The MTT assay showed these analogs to exhibit no apparent cytotoxicity against Human A549 lung adenocarcinoma cells, human HepG2 hepatoma cells and human umbilical vein endothelial cells (HUVEC). The presented structural differences have the potential for aiding the development of species-specific antimicrobial compounds with minimal effects on human cells.

Background: Identifying sequence-structure motifs common to two RNAs can speed up the comparison of structural RNAs substantially. The core algorithm of the existent approach ExpaRNA solves this problem for a priori known input structures. However, such structures are rarely known; moreover, predicting them computationally is no rescue, since single sequence structure prediction is highly unreliable. Results: The novel algorithm ExpaRNA-P computes exactly matching sequence-structure motifs in entire Boltzmann-distributed structure ensembles of two RNAs; thereby we match and fold RNAs simultaneously, analogous to the well-known “simultaneous alignment and folding” of RNAs. While this implies much higher flexibility compared to ExpaRNA, ExpaRNA-P has the same very low complexity (quadratic in time and space), which is enabled by its novel structure ensemble-based sparsification. Furthermore, we devise a generalized chaining algorithm to compute compatible subsets of ExpaRNA-P’s sequence-structure motifs. Resulting in the very fast RNA alignment approach ExpLoc-P, we utilize the best chain as anchor constraints for the sequence-structure alignment tool LocARNA. ExpLoc-P is benchmarked in several variants and versus state-of-the-art approaches. In particular, we formally introduce and evaluate strict and relaxed variants of the problem; the latter makes the approach sensitive to compensatory mutations. Across a benchmark set of typical non-coding RNAs, ExpLoc-P has similar accuracy to LocARNA but is four times faster (in both variants), while it achieves a speed-up over 30-fold for the longest benchmark sequences (≈400nt). Finally, different ExpLoc-P variants enable tailoring of the method to specific application scenarios. ExpaRNA-P and ExpLoc-P are distributed as part of the LocARNA package. The source code is freely available at http://www.bioinf.uni-freiburg.de/Software/ExpaRNA-P webcite. Conclusions: ExpaRNA-P’s novel ensemble-based sparsification reduces its complexity to quadratic time and space. Thereby, ExpaRNA-P significantly speeds up sequence-structure alignment while maintaining the alignment quality. Different ExpaRNA-P variants support a wide range of applications.

RNA molecules form complex higher-order structures which are essential to perform their biological activities. The accurate prediction of an RNA secondary structure and other higher-order structural constraints will significantly enhance the understanding of RNA molecules and help interpret their functions. Covariation analysis is the predominant computational method to accurately predict the base pairs in the secondary structure of RNAs. I developed a novel and powerful covariation method, Phylogenetic Events Count (PEC) method, to determine the positional covariation. The application of the PEC method onto a bacterial 16S rRNA sequence alignment proves that it is more sensitive and accurate than other mutual information based method in the identification of base-pairs and other structural constraints of the RNA structure. The analysis also discoveries a new type of structural constraint – neighbor effect, between sets of nucleotides that are in proximity in the three dimensional RNA structure with weaker but significant covariation with one another. Utilizing these covariation methods, a proposed secondary structure model of an entire HIV-1 genome RNA is evaluated. The results reveal that vast majority of the predicted base pairs in the proposed HIV-1 secondary structure model do not have covariation, thus lack the support from comparative analysis. Generating the most accurate multiple sequence alignment is fundamental and essential of performing high-quality comparative analysis. The rapid determination of nucleic acid sequences dramatically increases the number of available sequences. Thus developing the accurate and rapid alignment program for these RNA sequences has become a vital and challenging task to decipher the maximum amount of information from the data. A template-based RNA sequence alignment system, CRWAlign-2, is developed to accurately align new sequences to an existing reference sequence alignment based on primary and secondary structural similarity. A comparison of CRWAlign-2 with eight alternative widely-used alignment programs reveals that CRWAlign-2 outperforms other programs in aligning new sequences with higher accuracy. In addition to aligning sequences accurately, CRWAlign-2 also creates secondary structure models for each sequence to be aligned, which provides very useful information for the comparative analysis of RNA sequences and structures. The CRWAlign-2 program also provides opportunities for multiple areas including the identification of chimeric 16S rRNA sequences generated in microbiome sequencing projects. / text

The objective of this study is to evaluate pseudo-substrates of pantothenate kinase (PanK) for the therapeutic treatment of multidrug resistant bacterial infections of Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Pantothenate (Pan) analogs, including N- pentylpantothenamide (N5-Pan) and N-heptylpantothenamide (N7-Pan), hamper bacterial growth by utilizing the PanK enzymes, which normally catalyze the rate determining step of the Coenzyme A biosynthetic pathway. Here we report the structures of SaPanK, Human PanK3 and EcPanK complexed with N7-Pan or N5-Pan, all of which have provided the opportunity to investigate the structural differences of bacterial and human Pan binding sites. The MTT assay showed these analogs to exhibit no apparent cytotoxicity against Human A549 lung adenocarcinoma cells, human HepG2 hepatoma cells and human umbilical vein endothelial cells (HUVEC). The presented structural differences have the potential for aiding the development of species-specific antimicrobial compounds with minimal effects on human cells.