Secondary structure (SS) refers to the local spatial organization of the polypeptide backbone atoms of a protein. Accurate prediction of SS is a vital clue to resolve the 3D structure of protein. SS has three different components- helix (H), beta (E) and coil (C). Most SS predictors are imbalanced as their accuracy in predicting helix and coil are high, however significantly low in the beta. The objective of this thesis is to develop a balanced SS predictor which achieves good accuracies in all three SS components. We proposed a novel approach to solve this problem by combining a genetic algorithm (GA) with a support vector machine. We prepared two test datasets (CB471 and N295) to compare the performance of our predictors with SPINE X. Overall accuracy of our predictor was 76.4% and 77.2% respectively on CB471 and N295 datasets, while SPINE X gave 76.5% overall accuracy on both test datasets.

Príons são proteínas que causam um grupo de doenças neurodegenerativas invariavelmente fatais, sendo uma das mais conhecidas a encefalopatia espongiforme bovina (ou doença da vaca louca). A proteína príon celular (PrPc), rica em estrutura α-helicoidal, sofre uma mudança na sua estrutura secundária produzindo a proteína patológica (PrPSc; o príon) na qual prevalecem folhas-β. Devido à falta de dados estruturais de alta resolução dos príons, simulações de dinâmica molecular (DM) podem ser particularmente úteis para estudar o redobramento de PrP. Estudos experimentais e computacionais, descritos na literatura, indicam que a utilização de pH ácido é capaz de criar certa instabilidade estrutural, produzindo um ganho de estrutura-β na região N-terminal antes desestruturada. Este trabalho se propõe a investigar computacionalmente as mudanças estruturais na proteína príon celular do hamster Sírio induzidas por alteração de pH. Para isso, foi avaliada a influência de diferentes campos de força (GROMOS96 53a6, GROMOS96 43a1, AMBER99SB, AMBER99SB-ILDN, CHARMM27 e OPLS) simulados para as condições de pH neutro e ácido. A partir das análises, observou-se uma forte dependência dos resultados com o campo de força empregado. Além disso, somente os campos de força GROMOS96 53a6 e AMBER99SB demonstraram tendência à expansão do núcleo de folhas-β na região N-terminal da proteína simulada sob pH ácido e conseguiram representar adequadamente a condição neutra. As estruturas correspondentes a esses campos de força em pH ácido, foram, então, utilizadas como ponto de partida para novas simulações de DM em pH neutro (pH 7,4). Essa situação de retorno ao pH neutro ocorre quando o príon sai do compartimento endossomal (submetido a pH ácido) e retorna à superfície externa celular (onde estaria submetida novamente a pH neutro). Os resultados desse estudo de retorno ao pH neutro apontaram para a não reversibilidade de PrPSc, com a manutenção da cauda N-terminal voltada para a extremidade N-terminal da α-hélice HB. / Prions are proteins that cause a group of invariably fatal neurodegenerative diseases, one of the most known being bovine spongiform encephalopathy (or mad cow disease). The cellular prion protein (PrPC), rich in α-helical structure, undergoes a change in its secondary structure producing the pathological protein (PrPSc; the prion) in which β-sheet prevails. Due to the lack of high resolution structural data of the prions, molecular dynamics simulations (MD) may be particularly useful to study the refolding of PrP. Experimental and computational studies, described in the literature, indicate that the use of acidic pH is capable to create some structural instability, producing a gain of β-structure in the previously unstructured N-terminal region. This work proposes to investigate computationally the structural changes in the cellular prion protein of the Syrian hamster induced by pH change. For this, the influence of different force fields (GROMOS96 53a6, GROMOS96 43a1, AMBER99SB, AMBER99SB-ILDN, CHARMM27 and OPLS) were evaluated for neutral and acid pH conditions. From the analysis, a strong dependence of the results with the force field was observed. In addition, only the GROMOS96 53a6 and AMBER99SB force fields showed a tendency to expand the β-sheet nucleus in the N-terminal region of the simulated protein under acid pH and were able to adequately represent the neutral condition. The structures corresponding to these force fields under acidic pH were then used as the starting point for new MD simulations under neutral pH. This situation of return to the neutral pH occurs when the prion leaves the endosomal compartment (submitted to acid pH) and returns to the external cellular surface (where it would be submitted again to neutral pH). The results of this neutral pH return study pointed to the non-reversibility of PrPSc, with the maintenance of the N-terminal tail facing the N-terminal end of the α-helix HB.

The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA molecules of mostly unknown function, as well as the discovery of novel non-coding RNA types and functional RNA elements. Here, we demonstrate that specific properties of graphs that represent the predicted RNA secondary structure reflect functional information. We introduce a computational algorithm and an associated web-based tool (GraPPLE) for classifying non-coding RNA molecules as functional and, furthermore, into Rfam families based on their graph properties. Unlike sequence-similarity-based methods and covariance models, GraPPLE is demonstrated to be more robust with regard to increasing sequence divergence, and when combined with existing methods, leads to a significant improvement of prediction accuracy. Furthermore, graph properties identified as most informative are shown to provide an understanding as to what particular structural features render RNA molecules functional. Thus, GraPPLE may offer a valuable computational filtering tool to identify potentially interesting RNA molecules among large candidate datasets.

Prediction of protein secondary structure from primary sequence of amino acids is a very challenging task, and the problem has been approached from several angles. Proteins have many different biological functions; they may act as enzymes or as building blocks (muscle fibers) or may have transport function (e.g., transport of oxygen). The three-dimensional protein structure determines the functional properties of the protein. A lot of interesting work has been done on this problem, and over the last 10 to 20 years the methods have gradually improved in accuracy. In this dissertation we investigate several techniques for predicting the protein secondary structure. The prediction is carried out mainly using pattern classification techniques such as neural networks, genetic algorithms, simulated annealing. Each individual algorithm may work well in certain situations but fails in others. Capitalizing on the positive decisions can be achieved by forcing the various methods to collaborate to reach a unified consensus based on their previous performances. The process of combining classifiers is called decision fusion. The various decision fusion techniques such as the committee method, correlation method and the Bayesian inference methods to fuse the solutions from various approaches and to get better prediction accuracy are thoroughly explored in this dissertation. The RS126 data set was used for training and testing purposes. The results of applying pattern classification algorithms along with decision fusion techniques showed improvement in the prediction accuracy compared to that of prediction by neural networks or pattern classification algorithms individually or combined with neural networks. This research has shown that decision fusion techniques can be used to obtain better protein secondary structure prediction accuracy.

In this thesis the relative importance of input encoding and learning algorithm on protein secondary structure prediction is explored. A novel input encoding, based on multidimensional scaling applied to a recently published amino acid substitution matrix, is developed and shown to be superior to an arbitrary input encoding. Both decimal valued and binary input encodings are compared. Two neural network learning algorithms, Resilient Propagation and Learning Vector Quantization, which have not previously been applied to the problem of protein secondary structure prediction, are examined. Input encoding is shown to have a greater impact on prediction accuracy than learning methodology with a binary input encoding providing the highest training and test set prediction accuracy.

The work describes the development of a new class of synthetic metallopeptides that features a dirhodium metal center. Combination of peptide and dirhodium properties leads to unique effects on peptide structure, peptide-protein interactions, and metal catalytic activity aimed at small molecule as well as protein substrates. Dirhodium is directly bound to carboxylate side chains of aspartate or glutamate yielding kinetically inert coordination complexes. This improves stability, allows purification and provides enhanced biocompatibility. Bridging of two side chains in the same sequence enables control of the peptide secondary structure. Dirhodium metallopeptides are applied to regulate coiled coil dimerization, stabilize and induce helical secondary structure, catalyze enantioselective organometallic transformation, and serve as ligands for proteins. These results lead to the development of hybrid organic-inorganic therapeutic agents, biological probes for study of protein-protein interactions, and enantioselective metallopeptide catalysis.

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

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.

In der vorliegenden Arbeit wird die Aufklärung der Konformation von Peptiden mit speziell modifizierten Aminosäuren beschrieben. Die Methoden der theoretischen Chemie (Quantenchemie, Molekülmechanik, Moleküldynamik) bilden dabei die Grundlage der Konformationsanalysen. Durch systematische Anwendung dieser Methoden werden im ersten Teil der Arbeit die konformativen Eigenschaften verschiedener [beta]-Aminosäuren und ihrer Oligomere ([beta]-Peptide) untersucht. Aus diesen Ergebnissen werden anschließend Regeln für das Sekundärstrukturdesign von ß-Peptiden abgeleitet. Der zweite Teil beschäftigt sich mit der theoretischen Konformationsanalyse von [alpha]- Hydrazinosäuren und ihrer Oligomere (Hydrazinopeptide). Aus den gewonnenen Erkenntnissen über die Ausbildung charakteristischer Sekundärstrukturelemente in diesen Verbindungen wird ebenfalls ein Regelwerk für das Design von Sekundärstrukturen aufgestellt. / The present work describes the conformational characteristics of pepttides with specifically modified amino acid constituents. For this purpose, the methods of theoretical chemistry (quantum chemistry, molecular mechanics, molecular dynamics) are utilisied for the conformational analyses. The conformation of various [beta]-amino acids and their oligomers ([beta]-peptides) are inverstigated in the first part of this work applying these methods. Rules for the design of definite secondary structures in [beta]-peptides are then derived from the obtained results. In the second part, systematic theoretical conformational analyses on [alpha]-hydrazino acids and their oligomers (hydrazino peptides) are described. The results are then used to compile a set of rules for the formation of characteriasitc secondary structures in this class of compounds.

Thesis (Ph.D. in Pharmaceutical Sciences) -- University of Colorado at Denver and Health Sciences Center, 2006. / Typescript. Includes bibliographical references (leaves 203-215). Free to UCDHSC affiliates. Online version available via ProQuest Digital Dissertations;