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<p> pH6DZ1 is a synthetic deoxyribozyme that is able to couple catalysis with fluorescence signal generation. This deoxyribozyme has the ability to cleave itself at a lone ribonucleotide that is present between a pair of deoxyribothymidines, one modified with a fluorophore (fluorescein) and the other with a quencher (DABCYL). Herein we report on the sequence truncation and secondary structure characterization ofpH6DZ1 as well as the identification of functionally important nucleotides within this deoxyribozyme. Our data indicate that pH6DZ1 has a four-way junction-like secondary structure comprised of four short duplexes, three hairpin loops, and three inter-helical unpaired elements. Ten nucleotides, all located in two separate single-stranded regions, were identified as functionally indispensable nucleotides. Nine nucleotides, most of which are also distributed in three single-stranded DNA elements, were identified as functionally vital nucleotides. Our study has shown that pH6DZ1 has a secondary structure that is more complex than those reported for other RNA-cleaving deoxyribozymes. A trans-acting DNA enzyme was also developed from the minimized version ofpH6DZl, which behaves as a true enzyme with a kcat value of~1 min"1 and generates a large fluorescence signal upon catalysis. This study should facilitate the future exploration of this unique DNAzyme for the development of DNAzyme-based biosensors. </p> / Thesis / Master of Science (MSc)

In this thesis I investigate the effect of amino acid substitutions on secondary structure of proteins. I found that the secondary structure is relatively resistant to mutations, some regions hold the same secondary structure, even though their sequences are very different. Since this effect was observed also for random sequences, I conclude that it is a general property of the amino acid sequence. The particular elements of secondary structures are differentially sensitive to the changes caused by mutations. Protein's sensitivity to mutations depends on the composition of its secondary structure. Some methods of secondary structure prediction are described in the introductory section.

Recurrent neural networks (RNNs) are state of the art sequential machine learning tools, but have difficulty learning sequences with long-range dependencies due to the exponential growth or decay of gradients backpropagated through the RNN. Some methods overcome this problem by modifying the standard RNN architecure to force the recurrent weight matrix W to remain orthogonal throughout training. The first half of this thesis presents a novel orthogonal RNN architecture that enforces orthogonality of W by parametrizing with a skew-symmetric matrix via the Cayley transform. We present rules for backpropagation through the Cayley transform, show how to deal with the Cayley transform's singularity, and compare its performance on benchmark tasks to other orthogonal RNN architectures. The second half explores two deep learning approaches to problems in RNA secondary structure inference and compares them to a standard structure inference tool, the nearest neighbor thermodynamic model (NNTM). The first uses RNNs to detect paired or unpaired nucleotides in the RNA structure, which are then converted into synthetic auxiliary data that direct NNTM structure predictions. The second method uses recurrent and convolutional networks to directly infer RNA base pairs. In many cases, these approaches improve over NNTM structure predictions by 20-30 percentage points.

The protein folding problem is examined. Specifically, the problem of predicting protein secondary structure from the amino acid sequence is investigated. A literature study is presented into the protein folding process and the different techniques that currently exist to predict protein secondary structures. These techniques include the use of expert rules, statistics, information theory and various computational intelligence techniques, such as neural networks, nearest neighbour methods, Hidden Markov Models and Support Vector Machines. A pattern recognition technique based on statistical analysis is developed to predict protein secondary structure from the amino acid sequence. The technique can be applied to any problem where an input pattern is associated with an output pattern and each element in both the input and output patterns can take its value from a set with finite cardinality. The technique is applied to discover the role that small sequences of amino acids play in the formation of protein secondary structures. By applying the technique, a performance score of Q8 = 59:2% is achieved, with a corresponding Q3 score of 69.7%. This compares well with state of the art techniques, such as OSS-HMM and PSIPRED, which achieve Q3 scores of 67.9% and 66.8% respectively, when predictions on single sequences are made. / Dissertation (MEng)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted

This thesis presents a new algorithm to predict the pre-microRNA secondary structure. An accurate prediction of the pre-microRNA secondary structure is important in miRNA informatics. Based on a recently proposed model, nucleotide cyclic motifs (NCM), to predict RNA secondary structure, we propose and implement a Modified NCM (MNCM) model with a physics-based scoring strategy to tackle the problem of pre-microRNA folding. Our microRNAfold is implemented using a global optimal algorithm based on the bottom-up local optimal solutions. It has been shown that studying the functions of multiple genes and predicting the secondary structure of multiple related microRNA is more important and meaningful since many polygenic traits in animals and plants can be controlled by more than a single gene. We propose a parallel algorithm based on the master-slave architecture to predict the secondary structure from an input sequence. The experimental results show that our algorithm is able to produce the optimal secondary structure of polycistronic microRNAs. The trend of speedups of our parallel algorithm matches that of theoretical speedups. Conserved secondary structures are likely to be functional, and secondary structural characteristics that are shared between endogenous pre-miRNAs may contribute toward efficient biogenesis. So identifying conserved secondary structure is very meaningful and identifying conserved characteristics in RNA is a very important research field. After the characteristics are extracted from the secondary structures of RNAs, corresponding patterns or rules could be dug out and used. We propose to use the conserved microRNA characteristics in two aspects: to improve prediction through knowledge base, and to classify the real specific microRNAs from pseudo microRNAs. Through statistical analysis of the performance of classification, we verify that the conserved characteristics extracted from microRNAs’ secondary structures are precise enough. Gene suppression is a powerful tool for functional genomics and elimination of specific gene products. However, current gene suppression vectors can only be used to silence a single gene at a time. So we design an efficient poly-cistronic microRNA vector and the web-based tool allows users to design their own microRNA vectors online.

L'acide désoxyribonucléique (ADN) et l'acide ribonucléique (ARN) sont des polymères de nucléotides essentiels à la cellule. À l'inverse de l'ADN qui sert principalement à stocker l'information génétique, les ARN sont impliqués dans plusieurs processus métaboliques. Par exemple, ils transmettent l’information génétique codée dans l’ADN. Ils sont essentiels pour la maturation des autres ARN, la régulation de l’expression génétique, la prévention de la dégradation des chromosomes et le ciblage des protéines dans la cellule. La polyvalence fonctionnelle de l'ARN résulte de sa plus grande diversité structurale. Notre laboratoire a développé MC-Fold, un algorithme pour prédire la structure des ARN qu'on représente avec des graphes d'interactions inter-nucléotidiques. Les sommets de ces graphes représentent les nucléotides et les arêtes leurs interactions. Notre laboratoire a aussi observé qu'un petit ensemble de cycles d'interactions à lui seul définit la structure de n'importe quel motif d'ARN. La formation de ces cycles dépend de la séquence de nucléotides et MC-Fold détermine les cycles les plus probables étant donnée cette séquence. Mon projet de maîtrise a été, dans un premier temps, de définir une base de données des motifs structuraux et fonctionnels d'ARN, bdMotifs, en terme de ces cycles. Par la suite, j’ai implanté un algorithme, MC-Motifs, qui recherche ces motifs dans des graphes d'interactions et, entre autres, ceux générés par MC-Fold. Finalement, j’ai validé mon algorithme sur des ARN dont la structure est connue, tels que les ARN ribosomaux (ARNr) 5S, 16S et 23S, et l'ARN utilisé pour prédire la structure des riborégulateurs. Le mémoire est divisé en cinq chapitres. Le premier chapitre présente la structure chimique, les fonctions cellulaires de l'ARN et le repliement structural du polymère. Dans le deuxième chapitre, je décris la base de données bdMotifs. Dans le troisième chapitre, l’algorithme de recherche MC-Motifs est introduit. Le quatrième chapitre présente les résultats de la validation et des prédictions. Finalement, le dernier chapitre porte sur la discussion des résultats suivis d’une conclusion sur le travail. / Deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) are polymers of nucleotides essential for the survival of the cell. Contrary to DNA, whose main role is to store genetic information, RNA is involved in multiple metabolic processes. For example, RNA is involved in the transfer of information from DNA to protein, the processing and modification of other RNAs, the regulation of gene expression, the end-maintenance of chromosomes, and the sorting of proteins within the cell. This functional versatility of RNA comes from its structural diversity. Our laboratory developed MC-Fold, an algorithm that predicts RNA structures by representing them with nucleotide interaction graphs. The nodes in these graphs represent the nucleotides, and the edges the interactions between them. Our laboratory also observed that a limited number of interaction cycles can define the structure of any RNA motif. The formation of these cycles is determined by the nucleotide sequence and MC-Fold determines the most likely cycles based on that sequence. In this Master Degree project, I first built a database of structural and functional RNA motifs, bdMotifs, based on their constituent cycles. Then, I implemented an algorithm, MC-Motifs, which detects motifs within interaction graphs generated either by MC-Fold or by any other method. Finally, I validated my algorithm on known RNA structures such as the 5S, 16S and 23S ribosomal RNA (rRNA) and predicted structure of riboswitches. The Master thesis is divided into five chapters. The first chapter presents the chemical structure of RNA, its cellular functions and the structural folding of the polymer. In the second chapter, the database bdMotifs is described. In the third chapter, the MC-Motifs algorithm is introduced. In the fourth chapter, I present the results of MC-Motifs. Finally, in the last chapter, I discuss theses results and I give a conclusion on the project.

Pre-mRNA splicing is a highly regulated cellular process. The tight cooperation of spliceosome and other splicing factors that enable pre-mRNA cis-elements interpretation results in precise pre-mRNA splicing regulation. Short conserved splicing sequences within introns represent an elementary and indispensable element for intron removal from primary transcript, yet they are not sufficient signals for efficient splicing events. Additional pre-mRNA features affect complex splicing regulation. We took advantage of strains with slightly disrupted spliceosome (prp45(1-169)) to study the effect of ACT1 and MAF1 intronic sequences on splicing efficiency. Here we show, that ACT1 intron region between branch point (BP) and 3' splice site (3'ss) maintains splicing efficiency in mutant cells. However, the specific element within this region was not determined. In addition, results implicate an alternative BP in splicing efficiency modulation in yeast Saccharomyces cerevisiae. Interestingly, this alternative BP is localized in ACT1 intron outside of the BP-3'ss region. Furthermore, splicing factors with potential influence on 3'ss selection were studied. Heterodimer composed of Slu7p and Prp18p participates in 3'ss positioning to the active site of the spliceosome. Splicing analysis of substrates with two...

Doctor of Philosophy / Department of Chemical Engineering / Jennifer L. Anthony / Ionic liquids are low melting salts composed of an organic cation and an inorganic or organic anion. Ionic liquids are of interest for their wide range of applications and unique properties, such as the negligible vapor pressure of some types of ionic liquids, and the ability to modify ionic liquid properties by selection of the cation or anion. It has been hypothesized that over one million binary ionic liquids (meaning a single cation/anion pair) are possible. Due to the vast number of potential combinations, it should be possible to design ionic liquids specifically for an application of interest. One potential application is their use as protein solvents. However there is little understanding of how ionic liquids affect proteins. This research examined the solubility and secondary structure of the hydrophobic corn protein zein in seven ionic liquids and three conventional solvents as a function of temperature and solvent properties. Zein’s solubility in the solvents was measured gravimetrically from 30 to 60 degrees Celsius. Solubility was then related to solvent properties to gain an understanding of what solvent properties are important, and how to design an ionic liquid to dissolve zein. It was found that a good solvent for zein has a small molecular volume, a low polarity, and is a weak hydrogen bond acceptor. Infrared spectroscopy with curve fitting was used to examine the secondary structure of zein as a function of both solvent and temperature from 25 to 95 degrees Celsius. It was found that most of the ionic liquids change zein’s secondary structure, but those secondary structure changes were not affected by temperature. Aprotic ionic liquids increase the amount of β-turn secondary structure through non-polar interactions between the mixed aromatic-alkyl imidazolium cations and the non-polar portions of the zein. Strong hydrogen bond accepting molecules were found to increase the amount of β-turn secondary structure. It is hypothesized from this research that suitable solvents for zein will have a small molar volume, low polarity, and be poor hydrogen bond acceptors. This combination of properties will enhance zein’s solubility and limit secondary structure changes that can harm protein properties.