Thesis (LLD)--University of Pretoria, 2009. / Summaries in English and Afrikaans. Includes bibliographical references.

The aim of the present study is to identify the di®erentially expressed genes be- tween two di®erent conditions and apply it in predicting the class of new samples using the microarray data. Microarray data analysis poses many challenges to the statis- ticians because of its high dimensionality and small sample size, dubbed as "small n large p problem". Microarray data has been extensively studied by many statisticians and geneticists. Generally, it is said to follow a normal distribution with equal vari- ances in two conditions, but it is not true in general. Since the number of replications is very small, the sample estimates of variances are not appropriate for the testing. Therefore, we have to consider the Bayesian approach to approximate the variances in two conditions. Because the number of genes to be tested is usually large and the test is to be repeated thousands of times, there is a multiplicity problem. To remove the defect arising from multiple comparison, we use the False Discovery Rate (FDR) correction. Applying the hypothesis test repeatedly gene by gene for several thousands of genes, there is a great chance of selecting false genes as di®erentially expressed, even though the signi¯cance level is set very small. For the test to be reliable, the probability of selecting true positive should be high. To control the false positive rate, we have applied the FDR correction, in which the p -values for each of the gene is compared with its corresponding threshold. A gene is, then, said to be di®erentially expressed if the p-value is less than the threshold. We have developed a new method of selecting informative genes based on the Bayesian Version of Behrens-Fisher distribution which assumes the unequal variances in two conditions. Since the assumption of equal variances fail in most of the situation and the equal variance is a special case of unequal variance, we have tried to solve the problem of ¯nding di®erentially expressed genes in the unequal variance cases. We have found that the developed method selects the actual expressed genes in the simulated data and compared this method with the recent methods such as Fox and Dimmic’s t-test method, Tusher and Tibshirani’s SAM method among others. The next step of this research is to check whether the genes selected by the pro- posed Behrens -Fisher method is useful for the classi¯cation of samples. Using the genes selected by the proposed method that combines the Behrens Fisher gene se- lection method with some other statistical learning methods, we have found better classi¯cation result. The reason behind it is the capability of selecting the genes based on the knowledge of prior and data. In the case of microarray data due to the small sample size and the large number of variables, the variances obtained by the sample is not reliable in the sense that it is not positive de¯nite and not invertible. So, we have derived the Bayesian version of the Behrens Fisher distribution to remove that insu±ciency. The e±ciency of this established method has been demonstrated by ap- plying them in three real microarray data and calculating the misclassi¯cation error rates on the corresponding test sets. Moreover, we have compared our result with some of the other popular methods, such as Nearest Shrunken Centroid and Support Vector Machines method, found in the literature. We have studied the classi¯cation performance of di®erent classi¯ers before and after taking the correlation between the genes. The classi¯cation performance of the classi¯er has been signi¯cantly improved once the correlation was accounted. The classi¯cation performance of di®erent classi¯ers have been measured by the misclas- si¯cation rates and the confusion matrix. The another problem in the multiple testing of large number of hypothesis is the correlation among the test statistics. we have taken the correlation between the test statistics into account. If there were no correlation, then it will not a®ect the shape of the normalized histogram of the test statistics. As shown by Efron, the degree of the correlation among the test statistics either widens or shrinks the tail of the histogram of the test statistics. Thus the usual rejection region as obtained by the signi¯cance level is not su±cient. The rejection region should be rede¯ned accordingly and depends on the degree of correlation. The e®ect of the correlation in selecting the appropriate rejection region have also been studied.

The intraverbal is argued to be the most socially significant verbal operant and yet it is the least studied. Heal and Hanley (2011) suggest that different teaching strategies will lead to different rates of acquisition and child-preference with the tacting operant. This study continued this research into the realm of intraverbals, with focus on whether the embedded teaching strategy could be punishing on play or engaging in learning opportunities. The teaching strategies of discovery teaching, embedded prompting, and direct teaching were compared to see which strategy correlated with higher rates of acquisition and higher child preference. The study utilized a multi-element design by rapidly alternating teaching strategies while evaluating rate of acquisition and number of learning opportunities within the teaching strategies. Child preference was also demonstrated through card selection of associated teaching strategies in a concurrent chains agreement design. The teaching strategies differed in the amount of teacher directedness and taught intraverbal "Wh" questions. It was found through this study that embedded prompting did not punish play or the engagement in learning opportunities. The three participants preferred the three strategies differently and all participants were responding correctly the highest percentage of the time during the direct teaching contingencies by the end of the teaching sessions.

Pharmaceutical biotechnology has been emerging as a defined, increasingly important area of science dedicated to the discovery and delivery of drugs and therapies for the treatment of various human diseases. In contrast to the advancement in pharmaceutical biotechnology, current drug discovery efforts are facing unprecedented challenges. Difficulties in identifying novel drug targets and developing effective and safe drugs are closely related to the complexity of the network of interacting human proteins. Protein-protein interactions mediate virtually all cellular processes. Therefore both identification and understanding of protein-protein interactions are essential to the process of deciphering disease mechanisms and developing treatments. Unfortunately, our current knowledge and understanding of the human interactome is largely incomplete. Most of the unknown protein-protein interactions are expected to be weak and/or transient, hence are not easily identified. These unknown or uncharacterized interactions could affect the efficacy and toxicity of drug candidates, contributing to the high rate of failure. In an attempt to facilitate the ongoing efforts in drug discovery, we describe herein a series of novel methods and their applications addressing the broad topic of protein-protein interactions. We have developed a highly efficient site-specific protein cross-linking technology mediated by the genetically incorporated non-canonical amino acid L-DOPA to facilitate the identification and characterization of weak protein-protein interactions. We also established a protocol to incorporate L-DOPA into proteins in mammalian cells to enable in vivo site-specific protein cross-kinking. We then applied the DOPA-mediated cross-linking methodology to design a protein probe which can potentially serve as a diagnostic tool or a modulator of protein-protein interactions in vivo. To deliver such engineered proteins or other bioanalytical reagents into single live cells, we established a laser-assisted cellular nano-surgery protocol which would enable detailed observations of cell-to-cell variability and communication. Finally we investigated a possible experimental scheme to genetically evolve a fluorescent peptide, which has tremendous potential as a tool in cellular imaging and dynamic observation of protein-protein interactions in vivo. We aim to contribute to the discovery and development of new drugs and eventually to the overall health of our society by adding the technology above to the array of currently available bioanalytical tools. / text

eEF-2K, also known as CaM kinase-III, is an atypical protein kinase which negatively regulates the global rate of protein synthesis through the phosphorylation and inactivation of its substrate eEF-2. Recently eEF-2K has been validated as a novel target for anti-cancer therapy. However, a detailed understanding of the role of eEF-2K in cancer biology is unavailable. Mechanistic studies can often provide an understanding of enzyme function. Therefore, we determined the kinetic mechanism of eEF-2K using a peptide substrate (Acetyl-RKKYKFNEDTERRRFL-amide). We found that eEF-2K adopts a ternary-complex, steady state ordered mechanism, with ATP binding required before the peptide substrate. A good cellular inhibitor is required for elucidating the role of eEF-2K in cancer biology. To date, NH125 is the only inhibitor used to investigate the activity of eEF-2K in cells. Although it is reported as a specific inhibitor of eEF-2K, its exact mode of action has not been reported. Through in-vitro assays and cellular studies, we found that NH125 is a non-specific inhibitor of eEF-2K that blocks eEF-2 phosphorylation in cells. There is a great demand for specific inhibitors of eEF-2K. We developed a fluorescence high throughput assay system for eEF-2K. The assay utilizes the peptide substrate labeled with a Sox moiety whose phosphorylation can be monitored at 485 nm in the presence of magnesium. We also validated the assay in a screen of 30,000 compounds in 384 well plates. We found the assay to be robust and identified a relatively specific inhibitor of eEF-2K and determined its mechanism of action. We found it behaved as a slowly reversible inhibitor of eEF-2K with a two step inhibition mechanism - fast initial binding at the enzyme active site, followed by a slower inactivation step. We propose that the nitrile group on the compound binds to the active site thiol in the enzyme covalently forming a reversible thioimidate adduct to inactivate the enzyme. / text

The development of hybridoma technology nearly forty years ago set the foundation for the use of antibodies in the life sciences. Subsequent advances in recombinant DNA technology have allowed us to adapt antibody genes to various screening systems, greatly increasing the throughput and specialized applications for which these complex biomolecules can be adapted. While selection systems are a powerful tool for discovery and evolution, they can be slow and prone to unintended biases. We see computational approaches as an efficient process for rapid discovery and engineering of antibodies. This is particularly relevant for biodefense and emerging infectious disease applications, for which time is a valuable commodity. In the first chapter of this work, we examine computational protocols for ‘supercharging’ proteins. This process resurfaces the target protein, adding charged moieties to impart specialized functions such as thermoresistance and cell penetration. Current algorithms for resurfacing proteins are static, treating each mutation as an event within a vacuum. The net result is that while several variants can be created, each must be tested experimentally to ensure the resultant protein is functional. In many cases, the designed proteins were severely impaired or incapable of folding. We hypothesize that a more dynamic approach, keeping an eye on energetics and the consequences of mutations will yield a more efficient and robust method for supercharging, successfully adding charges to proteins while minimizing deleterious effects. We continue on this theme applying the successful algorithm to supercharging antibodies for increased function. Utilizing the MS2 model biosensor system, we rationally engineer charges onto the surface of an antibody fragment, increasing thermoresistance, minimizing destabilizing effects, and in some cases actually increasing affinity. Finally, we apply next-generation sequencing approaches to the rapid discovery of antibodies directed against the Zaire Ebolavirus species. We utilize a local immunization strategy to generate a polarized antibody repertoire that is then sequenced to provide a database of antigen-specific variants. This repertoire is probed in silico and individual antibodies selected for analysis, bypassing time- and resource-consuming selection experiments. / text

Kinetic target-guided synthesis (TGS) and in situ click chemistry are among unconventional discovery strategies having the potential to streamline the development of protein-protein interaction modulators (PPIMs). In kinetic TGS and in situ click chemistry, the target is directly involved in the assembly of its own potent, bidentate ligand from a pool of reactive fragments. Herein, we report the use and validation of kinetic TGS based on the sulfo-click reaction between thio acids and sulfonyl azides as a screening and synthesis platform for the identification of high-quality PPIMs. Starting from a randomly designed library consisting of nine thio acids and nine sulfonyl azides leading to eighty one potential acylsulfonamides, the target protein, Bcl-XL selectively assembled four PPIMs, acylsulfonamides SZ4TA2, SZ7TA2, SZ9TA1, and SZ9TA5, which have been shown to modulate Bcl-XL/BH3 interactions. To further investigate the Bcl-XL templation effect, control experiments were carried out using two mutants of Bcl-XL. In one mutant, phenylalanine Phe131 and aspartic acid Asp133, which are critical for the BH3 domain binding, have been substituted by alanines, while arginine Arg139, a residue identified to play a crucial role in the binding of ABT-737, a BH3 mimetic, has been replaced by an alanine in the other mutant. Incubation of these mutants with the reactive fragments and subsequent LC/MS-SIM analysis confirmed that these building block combinations yield the corresponding acylsulfonamides at the BH3 binding site, the actual "hot spot" of Bcl-XL. These results validate kinetic TGS using the sulfo-click reaction as a valuable tool for the straightforward identification of high-quality PPIMs. Protein-protein interactions of the Bcl-2 family have been extensively investigated and the anti-apoptotic proteins (Bcl-2, Bcl-XL, and Mcl-1) have been validated as crucial targets for the discovery of potential anti-cancer agents. At the outset, Bcl-2 and Bcl-XL were considered to play an important role in the regulation of apoptosis. Accordingly, several small molecule inhibitors targeting Bcl-2 and/or Bcl-XL proteins were primarily designed. A series of acylsulfonamides targeting these proteins were reported by Abbott laboratories, ABT-737 and ABT-263 being the most potent candidates. Remarkably, these molecules were found to exhibit weaker binding affinities against Mcl-1, another anti-apoptotic protein. Further experimental evidence suggests that, inhibitors targeting Mcl-1 selectively or in combination with other anti-apoptotic proteins would lead to desired therapeutic effect. As a result, numerous small molecules displaying activity against Mcl-1 have been identified so far. Specifically, acylsulfonamides derived from structure activity relationship by interligand nuclear overhauser effect (SAR by ILOEs), a fragment-based approach, have been recently reported with binding affinities in the nanomolar range. In the meantime, we have reported that the kinetic TGS approach can also be applied to identify acylsulfonamides as PPIMs targeting Bcl-XL. Taken together, structurally novel acylsulfonamides can be potentially discovered as Mcl-1 inhibitors using the kinetic TGS approach. Thus, a library of thirty one sulfonyl azides and ten thio acids providing three hundred and ten potential products was screened against Mcl-1 and the kinetic TGS hits were identified. Subsequently, control experiments involving Bim BH3 peptide were conducted to confirm that the fragments are assembled at the binding site of the protein. The kinetic TGS hits were then synthesized and subjected to the fluorescence polarization assay. Gratifyingly, activities in single digit micromolar range were detected, demonstrating that the sulfo-click kinetic TGS approach can also be used for screening and identification of acylsulfonamides as PPIMs targeting Mcl-1. The amide bond serves as one of nature's most fundamental functional group and is observed in a large number of organic and biological molecules. Traditionally, the amide functionality is introduced in a molecule through coupling of an amine and an activated carboxylic acid. Recently, various alternative methods have been reported wherein, the aldehydes or alcohols are oxidized using transition metal catalysts and are treated with amines to transform into the corresponding amides. These transformations however, require specially designed catalysts, long reaction times and high temperatures. We herein describe a practical and efficient amidation reaction involving aromatic aldehydes and various azides under mild basic conditions. A broad spectrum of functional groups was tolerated, demonstrating the scope of the reaction. Consequently, the amides were synthesized in moderate to excellent yields, presenting an attractive alternative to the currently available synthetic methods.

Current home networks typically involve two or more machines sharing network resources. The vision for the home network has grown from a simple computer network, to every day appliances embedded with network capabilities. In this environment devices and services within the home can interoperate, regardless of protocol or platform. Network clients can discover required resources by performing network discovery over component descriptions. Common approaches to this discovery process involve simple matching of keywords or attribute/value pairings. Interest emerging from the Semantic Web community has led to ontology languages being applied to network domains, providing a logical and semantically rich approach to both describing and discovering network components. In much of the existing work within this domain, developers have focused on defining new description frameworks in isolation from existing protocol frameworks and vocabularies. This work proposes an ontology-based description framework which takes the ontology approach to the next step, where existing description frameworks are in- corporated into the ontology-based framework, allowing discovery mechanisms to cover multiple existing domains. In this manner, existing protocols and networking approaches can participate in semantically-rich discovery processes. This framework also includes a system architecture developed for the purpose of reconciling existing home network solutions with the ontology-based discovery process. This work also describes an implementation of the approach and is deployed within a home-network environment. This implementation involves existing home networking frameworks, protocols and components, allowing the claims of this work to be examined and evaluated from a ‘real-world’ perspective.

In efforts to search for tool compounds that are capable of probing normal and disease-associated biological processes, both quality and identity of the screening collection are very important. Towards this goal, diversity-oriented synthesis (DOS) has been explored for a decade, which aims to populate the chemical space with diverse sets of small molecules distinct from the traditional ones obtained via combinatorial chemistry. In the practice of DOS, macrocyclic ring-closing metathesis (RCM) reactions have been widely used. However, the prediction and control of stereoselectivity of the reaction is often challenging; chemical transformation of the olefin moiety within the product is in general limited. Chapter I of this thesis describes a methodology that addresses both problems simultaneously and thus extends the utility of the RCM reactions. By installing a silyl group at the internal position of one of the olefin termini, the RCM reaction could proceed with high stereoselectivity to afford the (E)-alkenylsiloxane regardless of the intrinsic selectivity of the substrate. The resulting alkenylsiloxane can be transformed to a variety of functionalities in a regiospecific fashion. The conversion of the (E)-alkenylsiloxanes to alkenyl bromides could proceed with inversion of stereochemistry for some substrates allowing the selective access of both the E- and Z-trisubstituted macrocyclic alkenes. It was also found that the silyl group could trap the desired mono-cyclized product by suppressing nonproductive pathways. Chapter II of this thesis describes the application of the concept of DOS in the area of fragment-based drug discovery. Most fragment libraries used to date have been limited to aromatic heterocycles with an underrepresentation of chiral, enantiopure, \(sp^3\)-rich compounds. In order to create a more diverse fragment collection, the build/couple/pair algorithm was adopted. Starting from proline derivatives, a series of bicyclic compounds were obtained with complete sets of stereoisomers and high \(sp^3\) ratio. Efforts are also described toward the generation of diverse fragments using methodology described in Chapter I. The glycogen synthase kinase \((GSK3\beta)\) was selected as the proof-of-concept target for screening the DOS fragments. / Chemistry and Chemical Biology

published_or_final_version / Education / Master / Master of Education