No description available.

Pharmaceutical solids tend to exist in different physical forms termed as polymorphs. Issues about pharmaceutical systems are mainly concerned with the active ingredient's physico-chemical stability and bioavailability. The main aim of this study is to investigate the non-bonded interactions in pharmaceutical solids that govern the physical pharmaceutics performance of such materials and through the use of structural techniques and correlation of these results with crystal structural database to establish the presence of physical motifs in selected systems. Structural motifs were identified by the use of single crystal and crystal packing analysis on diverse range of pharma-relevant materials including chalcones, cryptolepines, biguanides and xanthines. These selected systems were validated using functional group and molecular analysis and correlating them to the Cambridge Structural Database. Crystallization studies are done on these selected systems as well as exploiting those using synthetic analogues. A total of 51 crystal structures were investigated including 16 new structure determinations. Addition synthesis of new xanthines to investigate novel intermolecular patterns was also undertaken. The understanding and exploitation of intermolecular interactions involving hydrogen bonds and coordination complexation during packing can be used in the design and synthesis of solid state molecular structures with desired physical and chemical properties.

One of the most profound open questions in biology is how the genetic code developed. The blueprints for proteins are encoded by triplets of nucleic acids, which in turn require proteins for interpretation and replication. The mere existence of this self-referencing system is a chicken-and-egg dilemma. Aminoacyl-tRNA synthetases are key players in the transfer of genetic information and reflect the earliest episode of life. These enzymes are responsible for loading tRNA molecules with the correct amino acid. Two protein superfamilies of aminoacyl-tRNA synthetases emerged, each responsible for ten amino acids. Despite sequence and structure similarity, the delicate balance between these superfamilies is manifested in two structural motifs, which were identified in the context of this thesis: the Backbone Brackets and the Arginine Tweezers. Both motifs realize constant ligand recognition and can be found in almost all protein structures of aminoacyl-tRNA synthetases. In this thesis, I thoroughly characterized Backbone Brackets and Arginine Tweezers. The specific characteristics of these motifs require high-precision methods for their detection and analysis. However, existing algorithms do not feature an adequate computational representation of structural motifs at the atom level and the support of isofunctional residue mutations. In order to address these limitations, I designed the Fit3D algorithm for template-based and template-free detection of structural motifs. I show that proper computational representation of structural motifs is crucial and improves accuracy up to 26% for a benchmark dataset. Fit3D is a general-purpose tool for structural motif detection in high-resolution protein structure data. In conjunction with the accelerating progress in experimental methods, the demand for such tools will increase rapidly over the next years. I applied Fit3D to structures of aminoacyl-tRNA synthetases to investigate whether Backbone Brackets and Arginine Tweezers are universal building blocks for ligand recognition, and to quantify structural changes upon ligand binding. While the Arginine Tweezers motif is exclusively found in aminoacyl-tRNA synthetases and paralogs, the Backbone Brackets seem to be a general pattern to recognize functional groups of certain ligands. The results show subtle differences in side chain orientation for one structural motif and a backbone shift for the other. This suggests a structural rearrangement to be a general mechanism in some aminoacyl-tRNA synthetases. The detailed level of these analyses would not have been possible without high-precision structural motif detection with Fit3D. The results emphasize the importance of structural motifs, which consist of only a few residues, for the global function of the enzyme. Furthermore, the stunning conservation of the structural motifs located in the core domains of aminoacyl-tRNA synthetases suggests their presence in the earliest predecessors of these enzymes. Both motifs might have played a fundamental role in shaping the genetic code as we know it.

Pharmaceutical solids tend to exist in different physical forms termed as polymorphs. Issues about pharmaceutical systems are mainly concerned with the active ingredient's physico-chemical stability and bioavailability. The main aim of this study is to investigate the non-bonded interactions in pharmaceutical solids that govern the physical pharmaceutics performance of such materials and through the use of structural techniques and correlation of these results with crystal structural database to establish the presence of physical motifs in selected systems. Structural motifs were identified by the use of single crystal and crystal packing analysis on diverse range of pharma-relevant materials including chalcones, cryptolepines, biguanides and xanthines. These selected systems were validated using functional group and molecular analysis and correlating them to the Cambridge Structural Database. Crystallization studies are done on these selected systems as well as exploiting those using synthetic analogues. A total of 51 crystal structures were investigated including 16 new structure determinations. Addition synthesis of new xanthines to investigate novel intermolecular patterns was also undertaken. The understanding and exploitation of intermolecular interactions involving hydrogen bonds and coordination complexation during packing can be used in the design and synthesis of solid state molecular structures with desired physical and chemical properties.

<p>In summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process.</p>

<p>In summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process.</p>

Magister Scientiae - MSc / In summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process. / South Africa

No description available.

Dans cette thèse, l'emphase est mise sur la réconciliation de l'analyse de structure et de séquence pour les protéines. L'analyse de séquence brille lorsqu'il s'agit de comparer des protéines présentant une forte identité de séquence (≤ 30\%) mais laisse à désirer pour identifier des homologues lointains. L'analyse de structure est une alternative intéressante. Cependant, les méthodes de résolution de structures sont coûteuses et complexes - lorsque toutefois elles produisent des résultats. Ces observations rendent évident la nécessité de développer des méthodes hybrides, exploitant l'information extraite des structures disponibles pour l'injecter dans des modèles de séquence. Cette thèse produit quatre contributions principales dans ce domaine. Premièrement, nous présentons une nouvelle distance structurale, le RMSDcomb, basée sur des patterns de conservation structurale locale, les motifs structuraux. Deuxièmement, nous avons développé une méthode pour identifier des motifs structuraux entre deux structures exploitant un bootstrap dépendant de filtrations. Notre approche n'est pas un compétiteur direct des aligneurs flexibles mais permet plutôt de produire des analyses multi-échelles de similarités structurales. Troisièmement, nous exploitons les méthodes suscitées pour construire des modèles de Markov cachés hybrides biaisés vers des régions mieux conservées structurellement. Nous utilisons un tel modèle pour caractériser les protéines de fusion virales de classe II, une tâche particulièrement ardue du fait de leur faible identité de séquence et leur conservation structurale moyenne. Ce faisant, nous parvenons à trouver un certain nombre d'homologues distants connues des protéines virales, notamment chez la Drosophile. Enfin, en formalisant un sous-problème rencontré lors de la comparaison de filtrations, nous présentons un nouveau problème théorique - le D-family matching - sur lequel nous démontrons des résultats algorithmiques variés. Nous montrons - d'une façon analogue à la comparaison de régions de deux conformations d'une protéine - comment exploiter ce modèle théorique pour comparer deux clusterings d'un même jeu de données. / In this thesis, the focus is set on reconciling the realms of structure and sequence for protein analysis. Sequence analysis tools shine when faced with proteins presenting high sequence identity (≤ 30\%), but are lack - luster when it comes to remote homolog detection. Structural analysis tools present an interesting alternative, but solving structures - when at all possible- is a tedious and expensive process. These observations make the need for hybrid methods - which inject information obtained from available structures in a sequence model - quite clear. This thesis makes four main contributions toward this goal. First we present a novel structural measure, the RMSDcomb, based on local structural conservation patterns - the so called structural motifs. Second, we developed a method to identify structural motifs between two structures using a bootstrap method which relies on filtrations. Our approach is not a direct competitor to flexible aligners but can provide useful to perform a multiscale analysis of structural similarities. Third, we build upon the previous methods to design hybrid Hidden Markov Models which are biased towards regions of increased structural conservation between sets of proteins. We test this tool on the class II fusion viral proteins - particularly challenging because of their low sequence identity and mild structural homology. We find that we are able to recover known remote homologs of the viral proteins in the Drosophila and other organisms. Finally, formalizing a sub - problem encountered when comparing filtrations, we present a new theoretical problem - the D-family matching - on which we present various algorithmic results. We show - in a manner that is analogous to comparing parts of two protein conformations - how it is possible to compare two clusterings of the same data set using such a theoretical model.