A thesis submitted to the Faculty of Medicine, University of the Witwatersrand, Johannesburg in fulfilment of the requirements for the degree of Doctor of Philosophy Johannesburg, 1988

Thesis advisor: Evan R. Kantrowitz / Nucleotides, the building blocks for nucleic acids, are essential for cell growth and replication. In E. coli the enzyme responsible for the regulation of pyrimidine nucleotide biosynthesis is aspartate transcarbamoylase (ATCase), which catalyzes the committed step in this pathway. ATCase is allosterically inhibited by CTP and UTP in the presence of CTP, the end products of the pyrimidine pathway. ATP, the end product of the purine biosynthetic pathway, acts as an allosteric activator. ATCase undergoes the allosteric transition from the low-activity and low-affinity T state to the high-activity and high-affinity R state upon the binding of the substrates. In this work we were able to trap an intermediate ATCase along the path of the allosteric transition between the T and R states. Both the X-ray crystallography and small-angle X-ray scattering in solution clearly demonstrated that the mutant ATCase (K164E/E239K) exists in an intermediate quaternary structure shifted about one-third toward the canonical R structure from the T structure. The structure of this intermediate ATCase is helping to understand the mechanism of the allosteric transition on a molecular basis. In this work we also discovered that a metal ion, such as Mg2+, was required for the synergistic inhibition by UTP in the presence of CTP. Therefore, the metal ion also had significant influence on how other nucleotides effect the enzyme. A more physiological relevant model was proposed involving the metal ion. To better understand the allosteric transition of ATCase, time-resolved small-angle X-ray scattering was utilized to track the conformational changes of the quaternary structure of the enzyme upon reaction with the natural substrates, PALA and nucleotide effectors. The transition rate was increased with an increasing concentration of the natural substrates but became over one order of magnitude slower with addition of PALA. Addition of ATP to the substrates increased the rate of the transition whereas CTP or the combination of CTP and UTP exhibited the opposite effect. In this work we also studied E. coli dihydroorotase (DHOase), which catalyzes the following step of ATCase in the pyrimidine biosynthetic pathway. A virtual high throughput screening system was employed to screen for inhibitors of DHOase, which may become potential anti-proliferation and anti-malarial drug candidates. Upon the discovery of the different conformations of the 100's loop of DHOase when substrate or product bound at the active site, we've genetically incorporated an unnatural fluorescent amino acid to a site on this loop in the hope of obtaining a better understanding of the catalysis that may involve the movement of the 100's loop. / Thesis (PhD) — Boston College, 2012. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Chemistry.

Thesis (Ph. D.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (M.Sc.)--York University, 2004. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 98-100). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL:http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss&rft%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&rft%5Fdat=xri:pqdiss:MQ99401

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Thesis (M. Med. Sc.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 38-43).

Thesis (M. Med. Sc.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 16-19).

Identification of pathogens is one of the important duties of clinical microbiology laboratory. Traditionally, phenotypic tests are used to identify the bacteria. However, due to some limitations of the phenotypic tests, the bacteria may not be identified sometimes and cannot be identified promptly. 16S rRNA gene sequencing is a rapid and accurate method to achieve this target. It is especially useful for identify rare or slow growing bacteria. However, the interpretation of the 16S rRNA gene sequencing result is one of the challenging duties to laboratory technicians and microbiologists. Apart from the well known 16S rRNA gene databases such as Genbank, The Ribosomal Database Project (RDP-II), MicroSeq databases, Ribosomal Differentiation of medical Microorganism database (RIDOM), SmartGene IDNS, 16SpathDB is an automated and comprehensive database for interpret the 16S rRNA gene result. The 16SpathDB first version was established in 2011. In this study, 16SpathDB was updated based on the all clinical important bacteria present in the 10th edition of the Manual of Clinical Microbiology (MCM)(Versalovic. et al., 2011) into this new version of database, 16SpathDB 2.0. The database was evaluated by using 689 16S rRNA gene sequences from 689 complete genomes of medically important bacteria. Among the 689 16S rRNA gene sequences, none was wrongly identified by 16SpathDB 2.0, with 247 (35.8%) 16S rRNA gene sequences reported in only one single bacterial species with more than 98% nucleotide identity with the query sequence (category 1), 440 (63.9%) reported as more than one bacterial species having more than 98% nucleotide identity with the query sequence (category 2), 2 (0.3%) reported to the genus level (category 3), and none reported as “no species in 16SpathDB 2.0 found to be sharing high nucleotide identity to your query sequence” (category 4). 16SpathDB 2.0 is an updated, automated, user-friendly, efficient and accurate database for 16S rRNA gene sequence interpretation in clinical microbiology laboratories. / published_or_final_version / Microbiology / Master / Master of Medical Sciences

RNA plays an important role in molecular biology. RNA sequence comparison is an important method to analysis the gene expression. Since aligning RNA reads needs to handle gaps, mutations, poly-A tails, etc. It is much more difficult than aligning other sequences. In this thesis, we study the RNA-Seq align tools, the existing gene information database and how to improve the accuracy of alignment and predict RNA secondary structure. The known gene information database contains a lot of reliable gene information that has been discovered. And we note most DNA align tools are well developed. They can run much faster than existing RNA-Seq align tools and have higher sensitivity and accuracy. Combining with the known gene information database, we present a method to align RNA-Seq data by using DNA align tools. I.e. we use the DNA align tools to do alignment and use the gene information to convert the alignment to genome based. The gene information database, though updated daily, there are still a lot of genes and alternative splicings that hadn't been discovered. If our RNA align tool only relies on the known gene database, then there may be a lot reads that come from unknown gene or alternative splicing cannot be aligned. Thus, we show a combinational method that can cover potential alternative splicing junction sites. Combining with the original gene database, the new align tools can cover most alignments which are reported by other RNA-Seq align tools. Recently a lot of RNA-Seq align tools have been developed. They are more powerful and faster than the old generation tools. However, the RNA read alignment is much more complicated than other sequence alignment. The alignments reported by some RNA-Seq align tools have low accuracy. We present a simple and efficient filter method based on the quality score of the reads. It can filter most low accuracy alignments. At last, we present a RNA secondary prediction method that can predict pseudoknot(a type of RNA secondary structure) with high sensitivity and specificity. / published_or_final_version / Computer Science / Master / Master of Philosophy

The rapid development of high-throughput sequencing technology provides a new chance to extend the scale and resolution of genomic research. How to efficiently and accurately call genetic variants in single base level (germline single nucleotide polymorphisms (SNPs) or somatic single nucleotide variants (SNVs)) is the fundamental challenge in sequencing data analysis, because these variants reported to influence transcriptional regulation, alternative splicing, non-coding RNA regulation and protein coding. Many applications have been developed to tackle this challenge. However, the shallow depth and cellular heterogeneity make those tools cannot attain satisfactory accuracy, and the huge volume of sequencing data itself cause this process inefficient. In this dissertation, firstly the performance of prevalent reads aligners and SNP callers for second-generation sequencing (SGS) is evaluated. And due to the high GC-content, the significantly lower coverage and poorer SNP calling performance in the regulatory regions of human genome by SGS is investigated. To enhance the capability to call SNPs, especially within the lower-depth regions, a fast and accurate SNP detection (FaSD) program that uses a binomial distribution based algorithm and a mutation probability is proposed. Based on the comparison with popular software and benchmarked by SNP arrays and high-depth sequencing data, it is demonstrated that FaSD has the best SNP calling accuracy in the aspects of genotype concordance rate and AUC. Furthermore, FaSD can finish SNP calling within four hours for 10X human genome SGS data on a standard desktop computer. Lastly, combined with the joint genotype likelihoods, an updated version of FaSD is proposed to call the cancerous somatic SNVs between paired tumor and normal samples. With extensive assessments on various types of cancer, it is demonstrated that no matter benchmarked by the known somatic SNVs and germline SNPs from database, or somatic SNVs called from higher-depth data, FaSD-somatic has the best overall performance. Inherited and improved from FaSD, FaSD-somatic is also the fastest somatic SNV caller among current programs, and can finish calling somatic mutations within 14 hours for 50X paired tumor and normal samples on normal server. / published_or_final_version / Biochemistry / Doctoral / Doctor of Philosophy