Philosophiae Doctor - PhD / 2018, the International Agency for Research on Cancer (IARC) estimated that prostate cancer (PCa) was the second leading cause of death in males worldwide. The number of deaths are expected to raise by 50 % in the next decade. This rise is attributed to the shortcomings of the current diagnostic, prognostic, and therapeutic biomarkers used in the management of the disease. Therefore, research into more sensitive, specific and effective biomarkers is a requirement. The use of biomarkers in PCa diagnosis and management takes advantage of the genetic alterations and abnormalities that characterise the disease. In this regard, a microRNA, hsa-miR-5698 was identified in a previous study as a differentiating biomarker between prostate adenocarcinoma and bone metastasis. Six putative translational targets (CDKN1A, CTNND1, FOXC1, LRP8, ELK1 and BIRC2) of this microRNA were discovered using in silico approaches. The aim of this study was to analyse via expression profiling and characterization, the target genes of hsa-miR-5698 in order to determine their ability to act as putative dynamic network biomarkers for PCa. The study was conducted using a combined in silico and molecular approach. The in silico part of the study investigated the putative transcriptional effects of hsa-miR-5698 on the promotors of its translational targets, the correlation between hsa-miR-5698 and mRNA expression profiles as well as the co-expression analysis, pathway analysis and prognostic ability of the target genes. A number of computational software were employed for these purposes, including, R Studio, Trident algorithm, STRING, KEGG, MEME Suite, SurvExpress and ProGgene. The molecular part of the study involved expression profiling of the genes in two PCa cell line LNCaP and PC3 via qPCR.

Escherichia coli 5'-nucleotidase is a periplasmically localised enzyme capable of hydrolysing a broad range of substrates, including all 5'-ribo- and 5'-deoxyribonucleotides, uridine diphosphate sugars, and a number of synthetic substrates such as bis (r-nitrophenyl) phosphate. The enzyme has been shown to contain at least one zinc ion following purification, and to have two metal binding sites in the catalytic cleft. 5'-Nucleotidase activity is significantly stimulated by the addition of particular divalent metal ions, most notably cobalt which results in a 30-50 fold increase in activity. Significant sequence homology between the E. coli 5'-nucleotidase and members of the Ser/Thr protein phosphatase family in the catalytic site has lead to 5'-nucleotidase being included in this protein family. This thesis describes the development of a rapid purification methodology for milligram quantities of 5'-nucleotidase, and the investigation of a number of physical and biochemical properties of the enzyme with the aim of comparing these properties to those of certain catalytic site mutants. The molecular weight of the mature protein was estimated as 58219 daltons, with a specific activity for 5'-AMP, in the presence of 4 mM Co2+ and 13 mM Ca2+ at pH 6.0, of 730 mmol/min/mg. The presence of up to two zinc ions associated with the purified enzyme was observed using ICP-ES analysis, suggesting both metal ion binding sites are occupied by zinc in vivo, and some degree of displacement of zinc by cobalt could be observed. Mass spectrometry data, gathered at 60 and 70 mS orifice potential, suggested the presence of a small proportion of material with a mass 118 to 130 daltons greater than the main 5'-nucleotidase mass estimation. This study suggests that this mass difference, only evident at the lower orificepotential, is due to the presence of two zinc ions closely associated with 5'-nucleotidase. To account for the observed high level of activation of 5'-nucleotidase activity by particular divalent metal ions, this thesis describes a proposed model in which these divalent ions may displace the zinc ion at one of the metal ion binding sites. This displacement only occurs at one of the two metal ion binding sites, with the other metal binding site retaining the zinc ion already present. Studies with purified enzyme, each with a single amino acid substitution, lend support to this hypothesis and suggest the identity of the metal ion binding site at which displacement occurs. Seven key catalytic site residues (Asp-41, His-43, Asp-84, His-117, Glu-118, His-217 and His-252) were selected on the basis of sequence conservation within the Ser/Thr protein phosphatases and 5'-nucleotidases. X-ray crystallographic data published by others during this study implicated five of the selected residues (Asp-41, His-43, Asp-84, His-217 and His-252) directly in metal ion binding, including two residues from each metal ion binding site and one directly involved in both sites (Asp-84). The remaining two residues (His-117 and Glu-118) are highly conserved but were not thought to play direct roles in metal ion binding. The seven selected residues were modified by site-directed mutagenesis, and the effect of the amino acid substitutions upon the kinetic properties of 5'-nucleotidase activity was determined. Residues hypothesised to be involved in metal ion displacement, and subsequent activation of 5'-nucleotidase activity, were identified by reductions in metal ion affinity and increased levels of activation by cobalt compared to the wild type 5'-nucleotidase. This study suggests that the metal binding site, M2, that includes residues Asp-84, His-217 and His-252, is involved in metal ion displacement, while the other metal binding site, M1, is not. This, in turn, suggests the metal binding sites are functionally non-equivalent and kinetically distinct. No residues were identified in this study as playing significant roles in substrate binding, as there was no significant reduction observed in affinity for 5'-AMP observed in any of the catalytic site mutants.

Protein tertiary structure plays a very important role in determining its possible functional sites and chemical interactions with other related proteins. Experimental methods to determine protein structure are time consuming and expensive. As a result, the gap between protein sequence and its structure has widened substantially due to the high throughput sequencing techniques. Problems of experimental methods motivate us to develop the computational algorithms for protein structure prediction. In this work, the clustering system is used to predict local protein structure. At first, recurring sequence clusters are explored with an improved K-means clustering algorithm. Carefully constructed sequence clusters are used to predict local protein structure. After obtaining the sequence clusters and motifs, we study how sequence variation for sequence clusters may influence its structural similarity. Analysis of the relationship between sequence variation and structural similarity for sequence clusters shows that sequence clusters with tight sequence variation have high structural similarity and sequence clusters with wide sequence variation have poor structural similarity. Based on above knowledge, the established clustering system is used to predict the tertiary structure for local sequence segments. Test results indicate that highest quality clusters can give highly reliable prediction results and high quality clusters can give reliable prediction results. In order to improve the performance of the clustering system for local protein structure prediction, a novel computational model called Clustering Support Vector Machines (CSVMs) is proposed. In our previous work, the sequence-to-structure relationship with the K-means algorithm has been explored by the conventional K-means algorithm. The K-means clustering algorithm may not capture nonlinear sequence-to-structure relationship effectively. As a result, we consider using Support Vector Machine (SVM) to capture the nonlinear sequence-to-structure relationship. However, SVM is not favorable for huge datasets including millions of samples. Therefore, we propose a novel computational model called CSVMs. Taking advantage of both the theory of granular computing and advanced statistical learning methodology, CSVMs are built specifically for each information granule partitioned intelligently by the clustering algorithm. Compared with the clustering system introduced previously, our experimental results show that accuracy for local structure prediction has been improved noticeably when CSVMs are applied.

Protein sequence motifs are gathering more and more attention in the field of sequence analysis. The recurring patterns have the potential to determine the conformation, function and activities of the proteins. In our work, we obtained protein sequence motifs which are universally conserved across protein family boundaries. Therefore, unlike most popular motif discovering algorithms, our input dataset is extremely large. As a result, an efficient technique is essential. We use two granular computing models, Fuzzy Improved K-means (FIK) and Fuzzy Greedy K-means (FGK), in order to efficiently generate protein motif information. After that, we develop an efficient Super Granular SVM Feature Elimination model to further extract the motif information. During the motifs searching process, setting up a fixed window size in advance may simplify the computational complexity and increase the efficiency. However, due to the fixed size, our model may deliver a number of similar motifs simply shifted by some bases or including mismatches. We develop a new strategy named Positional Association Super-Rule to confront the problem of motifs generated from a fixed window size. It is a combination approach of the super-rule analysis and a novel Positional Association Rule algorithm. We use the super-rule concept to construct a Super-Rule-Tree (SRT) by a modified HHK clustering, which requires no parameter setup to identify the similarities and dissimilarities between the motifs. The positional association rule is created and applied to search similar motifs that are shifted some residues. By analyzing the motifs results generated by our approaches, we realize that these motifs are not only significant in sequence area, but also in secondary structure similarity and biochemical properties.

For self-renewal, embryonic stem cells (ESCs) require the expression of specific transcription factors accompanied by a particular chromosome organization to maintain a balance between pluripotency and the capacity for rapid differentiation. However, how transcriptional regulation is linked to chromosome organization in ESCs is not well understood. Here we show that the cohesin component RAD21 exhibits a functional role in maintaining ESC identity through association with the pluripotency transcriptional network. ChIP-seq analyses of RAD21 reveal an ESC specific cohesin binding pattern that is characterized by CTCF independent co-localization of cohesin with pluripotency related transcription factors Oct4, Nanog, Sox2, Esrrb and Klf4. Upon ESC differentiation, most of these binding sites disappear and instead new CTCF independent RAD21 binding sites emerge, which are enriched for binding sites of transcription factors implicated in early differentiation. Furthermore, knock-down of RAD21 causes expression changes that are similar to expression changes after Nanog depletion, demonstrating the functional relevance of the RAD21 - pluripotency transcriptional network association. Finally, we show that Nanog physically interacts with the cohesin or cohesin interacting proteins STAG1 and WAPL further substantiating this association. Based on these findings we propose that a dynamic placement of cohesin by pluripotency transcription factors contributes to a chromosome organization supporting the ESC expression program.

For self-renewal, embryonic stem cells (ESCs) require the expression of specific transcription factors accompanied by a particular chromosome organization to maintain a balance between pluripotency and the capacity for rapid differentiation. However, how transcriptional regulation is linked to chromosome organization in ESCs is not well understood. Here we show that the cohesin component RAD21 exhibits a functional role in maintaining ESC identity through association with the pluripotency transcriptional network. ChIP-seq analyses of RAD21 reveal an ESC specific cohesin binding pattern that is characterized by CTCF independent co-localization of cohesin with pluripotency related transcription factors Oct4, Nanog, Sox2, Esrrb and Klf4. Upon ESC differentiation, most of these binding sites disappear and instead new CTCF independent RAD21 binding sites emerge, which are enriched for binding sites of transcription factors implicated in early differentiation. Furthermore, knock-down of RAD21 causes expression changes that are similar to expression changes after Nanog depletion, demonstrating the functional relevance of the RAD21 - pluripotency transcriptional network association. Finally, we show that Nanog physically interacts with the cohesin or cohesin interacting proteins STAG1 and WAPL further substantiating this association. Based on these findings we propose that a dynamic placement of cohesin by pluripotency transcription factors contributes to a chromosome organization supporting the ESC expression program.

The work deals with the fuzzy classification of DNA sequences. In the first part the theory summarized information about Fuzzy logic and methods of its use in the classification of biological sequence data. The second part is practically deal with the classification algorithm for assessing the similarity of sequences. Specifically, the dividing of coding and non-coding parts of the sequence and the use of fuzzy classification in DNA barcoding.