Since the dawn of the genomics era, the genetics of numerous human disorders has been understood which has led to improvements in targeted therapeutics. However, the focus of most research has been primarily on protein coding genes, which account for only 2% of the entire genome, leaving much of the remaining genome relatively unstudied. In particular, repetitive sequences, called microsatellites (MST), which are tandem repeats of 1 to 6 bases, are known to be mutational hotspots and have been linked to diseases, such as Huntington disease and Fragile X syndrome. This work represents a significant effort towards closing this knowledge gap. Specifically, we developed a next generation sequencing based enrichment method along with the supporting computational pipeline for detecting novel MST sequences in the human genome. Using this global MST enrichment protocol, we have identified 790 novel sequences. Analysis of these novel sequences has identified previously unknown functional elements, demonstrating its potential for aiding in the completion of the euchromatic DNA. We also developed a disease risk diagnostic using a novel target specific enrichment method that produces high resolution MST sequencing data that has the potential to validate, for the first time, the link between MST genotype variation and cancer. Combined with publicly available exome datasets of non-small cell lung cancer and 1000 genomes project, the target specific MST enrichment method uncovered a signature set of 21 MST loci that can differentiate between lung cancer and non-cancer control samples with a sensitivity ratio of 0.93. Finally, to understand the molecular causes of MST instability, we analyzed genomic variants and gene expression data for an autosomal recessive disorder, Fanconi anemia (FA). This first of its kind study quantified the heterogeneity of FA cells and demonstrated the possibility of utilizing the DNA crosslink repair dysfunctional FA cells as a suitable system to further study the causes of MST instability. / Ph. D.

Product defects concern various groups of people, such as customers, manufacturers, government officials, etc. Thus, defect-related knowledge and information are essential. In keeping with the growth of social media, online forums, and Internet commerce, people post a vast amount of feedback on products, which forms a good source for the automatic acquisition of knowledge about defects. However, considering the vast volume of online reviews, how to automatically identify critical product defects and summarize the related information from the huge number of user reviews is challenging, even when we target only the negative reviews. As a kind of opinion mining research, existing defect discovery methods mainly focus on how to classify the type of product issues, which is not enough for users. People expect to see defect information in multiple facets, such as product model, component, and symptom, which are necessary to understand the defects and quantify their influence. In addition, people are eager to seek problem resolutions once they spot defects. These challenges cannot be solved by existing aspect-oriented opinion mining models, which seldom consider the defect entities mentioned above. Furthermore, users also want to better capture the semantics of review text, and to summarize product defects more accurately in the form of natural language sentences. However, existing text summarization models including neural networks can hardly generalize to user review summarization due to the lack of labeled data. In this research, we explore topic models and neural network models for product defect discovery and summarization from user reviews. Firstly, a generative Probabilistic Defect Model (PDM) is proposed, which models the generation process of user reviews from key defect entities including product Model, Component, Symptom, and Incident Date. Using the joint topics in these aspects, which are produced by PDM, people can discover defects which are represented by those entities. Secondly, we devise a Product Defect Latent Dirichlet Allocation (PDLDA) model, which describes how negative reviews are generated from defect elements like Component, Symptom, and Resolution. The interdependency between these entities is modeled by PDLDA as well. PDLDA answers not only what the defects look like, but also how to address them using the crowd wisdom hidden in user reviews. Finally, the problem of how to summarize user reviews more accurately, and better capture the semantics in them, is studied using deep neural networks, especially Hierarchical Encoder-Decoder Models. For each of the research topics, comprehensive evaluations are conducted to justify the effectiveness and accuracy of the proposed models, on heterogeneous datasets. Further, on the theoretical side, this research contributes to the research stream on product defect discovery, opinion mining, probabilistic graphical models, and deep neural network models. Regarding impact, these techniques will benefit related users such as customers, manufacturers, and government officials. / Ph. D. / Product defects concern various groups of people, such as customers, manufacturers, and government officials. Thus, defect-related knowledge and information are essential. In keeping with the growth of social media, online forums, and Internet commerce, people post a vast amount of feedback on products, which forms a good source for the automatic acquisition of knowledge about defects. However, considering the vast volume of online reviews, how to automatically identify critical product defects and summarize the related information from the huge number of user reviews is challenging, even when we target only the negative reviews. People expect to see defect information in multiple facets, such as product model, component, and symptom, which are necessary to understand the defects and quantify their influence. In addition, people are eager to seek problem resolutions once they spot defects. Furthermore, users also want to better summarize product defects more accurately in the form of natural language sentences. These requirements cannot be satisfied by existing methods, which seldom consider the defect entities mentioned above, or hardly generalize to user review summarization. In this research, we develop novel Machine Learning (ML) algorithms for product defect discovery and summarization. Firstly, we study how to identify product defects and their related attributes, such as Product Model, Component, Symptom, and Incident Date. Secondly, we devise a novel algorithm, which can discover product defects and the related Component, Symptom, and Resolution, from online user reviews. This method tells not only what the defects look like, but also how to address them using the crowd wisdom hidden in user reviews. Finally, we address the problem of how to summarize user reviews in the form of natural language sentences using a paraphrase-style method. On the theoretical side, this research contributes to multiple research areas in Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning. Regarding impact, these techniques will benefit related users such as customers, manufacturers, and government officials.

Kinases are involved in a multitude of signaling pathways, such as cellular growth, proliferation, and apoptosis, and have been discovered to be important in numerous diseases including cancer, Alzheimer's disease, cardiovascular health, rheumatoid arthritis, and fibrosis. Due to the involvement in a wide variety of disease types, kinases have been studied for exploitation and use as targets for therapeutics. There are many limitations with developing kinase target therapeutics due to the high similarity of kinase active site composition, making the utilization of new techniques to determine kinase exploitability for therapeutic design with high specificity essential for the advancement of novel drug strategies. In silico approaches have become increasingly prevalent for providing useful insight into protein structure-function relationships, offering new information to researchers about drug discovery strategies. This work utilizes streamlined computational techniques on an atomistic level to aid in the identification of orthologue and isoform exploitability, identifying new features to be utilized for future inhibitor design. By exploring two separate kinases and kinase targeting domains, we found that orthologues and isoforms contain distinct features, likely responsible for their biological roles, which can be utilized and exploited for selective drug development. In this work, we identified new exploitable features between kinase orthologues for treatment in Human African Trypanosomiasis and structural morphology differences between two kinase isoforms that can potentially be exploited for cancer therapeutic design. / Master of Science in Life Sciences / Numerous diseases such as cancer, Alzheimer's disease, cardiovascular disease, rheumatoid arthritis, and fibrosis have been attributed to different cell growth and survival pathways. Many of these pathways are controlled by a class of enzymes called kinases. Kinases are involved in almost every metabolic pathway in human cells and can act as molecular switches to turn on and off disease progression. Due to the involvement of these kinases' in a wide variety of disease types, kinases have been continually studied for the development of new drugs. Developing effective drugs for kinases requires an extensive understanding of the structural characteristics due to the high structural similarity across all kinases. In silico, or computational, techniques are useful strategies for drug development practices, offering new information into protein structure-function relationships, which in turn can be utilized in drug discovery advancements. Utilizing computational methods to explore structural features can help identify specific protein structural features, thus providing new strategies for protein specific inhibitor design. In this work, we identified new exploitable features between kinase orthologues for treatment in Human African Trypanosomiasis and structural morphology differences between two kinase isoforms that can potentially be exploited for cancer therapeutic design.

Peroxisome proliferator-activated receptor γ (PPARγ) is associated with a wide range of diseases, including type 2 diabetes mellitus (T2D). Thiazolidinediones (TZDs) are agonists of PPARγ which have an insulin sensitizing effect, and are therefore used as a treatment for T2D. However, TZDs cause negative side effects in patients, such as weight gain, edema, and increased risk of bone fracture. Partial agonists could be an alternative to TZD-based drugs with fewer side effects. However, there is a lack of understanding of the types of PPARγ partial agonists and how they differ from full agonists. In silico techniques, like virtual screening, molecular docking, and pharmacophore modeling, allow us to determine and characterize markers of varying levels of agonism. An extensive search of the RCSB Protein Data Bank found 62 structures of PPARγ resolved with partial agonists. Cross-docking was performed and found that two PDB structures, 3TY0 and 5TWO, would be effective as receptor structures for virtual screening. By clustering known partial agonists by common pharmacophore features, we found several distinct groups of partial agonists. Interaction and pharmacophore models were created for each group of partial agonists. Virtual screening of FDA-approved compounds showed that the models were able to predict potential partial agonists of PPARγ. This study provides additional insight into the different binding modes of partial agonists of PPARγ and their characteristics. These models can be used to assist drug discovery efforts for intelligently designing novel therapeutics for T2D which have fewer negative side effects. / Master of Science in Life Sciences / The peroxisome proliferator-activated receptor γ (PPARγ) protein is associated with a wide range of diseases, including type 2 diabetes mellitus (T2D). Thiazolidinediones (TZDs) are compounds that activate PPARγ, and increase insulin sensitivity in patients with T2D. However, TZDs cause negative side effects in patients, such as weight gain, increased fluid retention, and increased risk of bone fracture. Partial agonists could be an alternative to TZD-based drugs with fewer side effects. However, there is a lack of understanding of the types of PPARγ partial agonists and how they differ from full agonists. Computational techniques allow us to investigate common features between known partial agonists. An extensive search of the RCSB Protein Data Bank found 62 structures of PPARγ which contained partial agonists. Each known partial agonist was docked into twelve complete PPARγ structures, and it was found that two structure models would be effective as receptor structures for virtual screening. A set of known partial agonists were grouped based on common chemical features, and three distinct groups of partial agonists were found. Binding criteria for each of these three groups were developed. A library of FDA-approved compounds was screened using the criteria for binding to identify potential novel partial agonists. Three potential novel partial agonists were found in the screening. This study provides additional insight into how different compounds activate PPARγ. These methods can be used to assist drug discovery efforts for intelligently designing novel therapeutics for T2D which have fewer negative side effects.

No / Since the development of the first cytotoxic agents, synthetic organic chemistry has advanced enormously. The synthetic and medicinal chemists of today are at the centre of drug development and are involved in most, if not all, processes of drug discovery. Recent decreases in government funding and reformed educational policies could, however, seriously impact on drug discovery initiatives worldwide. Not only could these changes result in fewer scientific breakthroughs, but they could also negatively affect the training of our next generation of medicinal chemists.

No / Aldehyde dehydrogenases (ALDHs) belong to a superfamily of 19 isozymes that are known to participate in many physiologically important biosynthetic processes including detoxification of specific endogenous and exogenous aldehyde substrates. The high expression levels of an emerging number of ALDHs in various cancer tissues suggest that these enzymes have pivotal roles in cancer cell survival and progression. Mapping out the heterogeneity of tumours and their cancer stem cell (CSC) component will be key to successful design of strategies involving therapeutics that are targeted against specific ALDH isozymes. This review summarises recent progress in ALDH-focused cancer research and discovery of small-molecule-based inhibitors.

No / Aim: A diagnostic test that could detect Treponema pallidum antigens in urine would facilitate the prompt diagnosis of syphilis. Materials & methods: Urine from 54 individuals with various clinical stages of syphilis and 6 controls were pooled according to disease stage and interrogated with complementary mass spectrometry techniques to uncover potential syphilis biomarkers. Results & conclusion: In total, 26 unique peptides were uncovered corresponding to four unique T. pallidum proteins that have low genetic sequence similarity to other prokaryotes and human proteins. This is the first account of direct T. pallidum protein detection in human clinical samples using mass spectrometry. The implications of these findings for future diagnostic test development is discussed. Data are available via ProteomeXchange with identifier PXD009707.

No / Accurate web log mining results and efficient online navigational pattern prediction are undeniably crucial for tuning up websites and consequently helping in visitors' retention. Like any other data mining task, web log mining starts with data cleaning and preparation and it ends up discovering some hidden knowledge which cannot be extracted using conventional methods. In order for this process to yield good results it has to rely on some good quality input data. Therefore, more focus in this process should be on data cleaning and pre-processing. On the other hand, one of the challenges facing online prediction is scalability. As a result any improvement in the efficiency of online prediction solutions is more than necessary. As a response to the aforementioned concerns we are proposing an enhancement to the web log mining process and to the online navigational pattern prediction. Our contribution contains three different components. First, we are proposing a refined time-out based heuristic for session identification. Second, we are suggesting the usage of a specific density based algorithm for navigational pattern discovery. Finally, a new approach for efficient online prediction is also suggested. The conducted experiments demonstrate the applicability and effectiveness of the proposed approach. (C) 2013 Elsevier B.V. All rights reserved.

Background and Aims Gliomas account for over half of all primary brain tumours and have a very poor prognosis, with a median survival of less than two years. There is an urgent and unmet clinical need to develop new therapies against glioma. Recent reports have indicated the overexpression of FPR1 in gliomas particularly in high grade gliomas. The aim of this project was to identify and synthesise small molecule FPR1 antagonists, and to demonstrate a proof of principle in preclinical in vitro and in vivo models that small molecule FPR1 antagonism can retard expansion of glioma. Methods A number of small molecule FPR1 antagonists were identified by in silico design, or from the literature and then were prepared using chemical synthesis. FPR1 antagonists were evaluated in vitro for their ability to abrogate FPR1-induced cellular responses in a range of models including calcium mobilisation, cell migration, and invasion. The efficacy of FPR1 antagonist ICT12035 in vivo was assessed in a U-87 MG subcutaneous xenograft model. Results Virtual high throughput screening using a homology model of FPR1 led to the identification of two small molecule FPR1 antagonists. At the same time chemical synthesis of two other antagonists, ICT5100 and ICT12035 as well as their analogues were carried out. The FPR1 antagonists were assessed in calcium flux assay which gave an insight into their structure-activity relationship. Further investigation of both ICT5100 and ICT12035 demonstrated that both small molecule FPR1 antagonists were effective at abrogating FPR1-induced calcium mobilisation, migration, and invasion in U- 87 MG in vitro models in a dose-dependent manner. ICT12035 is a particularly selective and potent inhibitor of FPR1 with an IC50 of 37.7 nM in calcium flux assay. Additionally, it was shown that the FPR1 antagonist ICT12035 was able to arrest the growth rate of U-87 MG xenografted tumours in mice. Conclusion The results demonstrate that targeting FPR1 by a small molecule antagonist such as ICT12035, could provide a potential new therapy for the treatment of glioblastoma. / Yorkshire Cancer Research

In recent years, advancements in computational methodologies have shed light on the complex process that makes proteins fold into their three-dimensional shapes. These new tools have helped us understand the steps proteins take to achieve these structures, revealing the presence of metastable intermediates along the folding pathways. This newfound understanding has led to the development of a novel drug discovery strategy known as Pharmacological Protein Inactivation by Folding Intermediate Targeting (PPI-FIT). This approach specifically targets folding intermediates to modulate protein expression levels, thus opening new opportunities for pharmacological intervention. This approach could be particularly relevant for diseases linked to targets that were previously considered "undruggable." A promising outcome of the PPI-FIT strategy is the identification of SM875, a compound that has been shown to lower prion protein (PrP) levels, positioning it as a potential therapeutic candidate for prion diseases. This study describes the initial phase of optimization of the SM875 scaffold. It encompasses the chemical diversification of SM875, followed by systematic evaluations of its biological activity and toxicity, with the aim of establishing structure-activity relationships (SAR). This knowledge is instrumental in guiding the synthesis of analogs with enhanced properties, advancing them through the development pipeline toward clinical application. Furthermore, this work investigates the potential regulatory function of folding intermediates in physiological processes, hypothesizing that they may serve as substrates for post translational modifications (PTMs). This hypothesis proposes an expansion of the current paradigm, suggesting that folding intermediates could constitute an additional layer of regulation within the complex network of proteostasis.