Thesis (M.Phil.) - University of Queensland, 2006. / Includes bibliography.

Thesis (M.Ch.E.)--University of Delaware, 2005. / Principal faculty advisor: Abraham M. Lenhoff, Dept. of Chemical Engineering. Includes bibliographical references.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Restricted as requested by author. Restricted access for 1 year 2007-03-31. Also available online.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 140-154).

Late SV40 factor (LSF) is a CP2 family transcription factor involved in cell cycle regulation. In liver cancer, LSF is an oncogene, in part due to its role in upregulation of osteopontin leading to increase tumor size. As a result, LSF is a potential target for drug discovery. LSF binds the p65 subunit of the transcription factor NFkB and also the transcription factor ying yang 1 (YY1). In this thesis, I show that binding of both YY1 and p65 occurs at the ubiquitin-like domain of LSF in U2OS cell extracts. Interestingly, when phosphatase inhibitors are added during preparation of U2OS cell extracts, the binding of YY1 and p65 to LSF shifts from the ubiquitin-like domain of LSF to the DNA binding domain. The role of a yet unidentified docking protein may be responsible for this shift in binding. In an attempt to map the specific region of the LSF sequence that is involved in these interactions, I have developed a peptide identification assay which utilizes protease digestion, protein mediated peptide capture, and LC ESI-MS. Through the use of this assay, I'm confident that the sequence(s) involved in these LSF protein-protein interactions can be further defined.

The recognition of small organic compounds and metabolites is essential for living systems, enabling the cell to sense environmental stimuli and respond appropriately. Developing quantitative models of living systems which can incorporate these environmental stimuli would accordingly benefit from comprehensive mapping of interactions between proteins and small molecules of interest. While high-throughput experimental methods provide a wealth of interaction data, the scale of chemical space currently precludes comprehensive enumeration of protein-compound interaction space. Computational methods can help to bridge this gap by inferring proteome-scale protein-compound interactomes, elucidating structural features within protein families which mediate specificity of binding to specific small molecules, and inferring the affinity of binding for specific protein-compound interactions. In this thesis, we attempt to use, and in some cases develop, methods to study protein-compound interactions at these three scales. First, we describe recent work in extending our structure-based algorithm for predicting protein-compound interactions throughout the proteome to include a wider array of small molecules. We demonstrate that this method performs comparably to existing methods and describe an online database storing the results of this analysis. We also report several case studies illustrating how this database can be used along with cautionary vignettes indicating areas where the method fails and directions for future improvement. We subsequently analyze druggable pockets occurring within protein-protein interfaces (PPIs) to assess whether they are less structurally conserved than analogous pockets of conventional drug sites. We find that PPI interfacial pockets are associated with fewer expected off-targets than conventional drug sites, however that this finding is specific to individual protein families, rather than a general feature of interfacial PPI pockets. Finally, we use Free Energy Perturbation to predict the binding affinity of an array of small volatile odorants with an olfactory receptor from the jumping bristletail, Machilis hrabei, as well as attempt to further optimize the system in order to study the effects of mutating receptor binding site residues on binding affinity to its active ligands.

The organization of biological processes via protein-protein interactions and the subcellular localization of enzymes is believed to be fundamental to many aspects of metabolism. Although this organization has been demonstrated in several systems, the mechanisms by which it is established and regulated are still not well understood. The flavonoid biosynthetic pathway offers a unique system in which to study several important aspects of metabolism. Here we describe a novel toolset of mutant alleles within the flavonoid biosynthetic pathway. In addition, we discuss the use of several of these alleles together with a number of emerging technologies to probe the role of subcellular localization of chalcone synthase, the first committed flavonoid biosynthetic enzyme, on metabolic flux, and to characterize a novel chalcone synthase-interacting protein. The over-expression of this interacting protein induces novel phenotypes that are likely associated with the production or distribution of auxin. Further, interaction analyses between recombinant flavonoid biosynthetic enzymes point to the possibility that post-translational modifications play an important role in promoting interactions. / Ph. D.

Thesis (M.S.)--University of Alabama at Birmingham, 2008. / Title from PDF title page (viewed Sept. 21, 2009). Additional advisors: Martha W. Bidez, W. Michael Carson, Richard A. Gray, W. William Wilson. Includes bibliographical references.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / William H. Hsu / The term 'protein-protein interaction (PPI)' refers to the study of associations between proteins as manifested through biochemical processes such as formation of structures, signal transduction, transport, and phosphorylation. PPI play an important role in the study of biological processes. Many PPI have been discovered over the years and several databases have been created to store the information about these interactions. von Mering (2002) states that about 80,000 interactions between yeast proteins are currently available from various high-throughput interaction detection methods. Determining PPI using high-throughput methods is not only expensive and time-consuming, but also generates a high number of false positives and false negatives. Therefore, there is a need for computational approaches that can help in the process of identifying real protein interactions. Several methods have been designed to address the task of predicting protein-protein interactions using machine learning. Most of them use features extracted from protein sequences (e.g., amino acids composition) or associated with protein sequences directly (e.g., GO annotation). Others use relational and structural features extracted from the PPI network, along with the features related to the protein sequence. When using the PPI network to design features, several node and topological features can be extracted directly from the associated graph. In this thesis, important graph features of a protein interaction network that help in predicting protein interactions are identified. Two previously published datasets are used in this study. A third dataset has been created by combining three PPI databases. Several classifiers are applied on the graph attributes extracted from protein interaction networks of these three datasets. A detailed study has been performed in this present work to determine if graph attributes extracted from a protein interaction network are more predictive than biological features of protein interactions. The results indicate that the performance criteria (such as Sensitivity, Specificity and AUC score) improve when graph features are combined with biological features.

The oncoprotein MDM2 has an integral role in cancer development via multiple signalling pathways. Two proteomic mass spectrometry screens, label-free with spectral counting quantitation and 8-plex iTRAQ were used to identify proteins up or downregulated over time by the MDM2 targeting drug Nutlin. A subset of previously identified MDM2 binding partners were identified as altered after Nutlin treatment, along with proteins which have not as yet been linked to MDM2 or p53. Proteins altered two hours after Nutlin treatment were screened for sequence similarity to an MDM2 binding consensus motif based on the BOX-I region of p53. Peptides corresponding to this motif were validated for MDM2 binding, and the mode of binding investigated using competition ELISA and thermal denaturation assays. Known MDM2 ligands such as Nutlin were shown to have a range of effects on the binding of these newly identified MDM2 peptides, which may be attributed to allosteric regulation of MDM2. The effects of Nutlin on two full length proteins identified by the MS screens, CypB and NPM, were confirmed in vivo. In vitro binding of MDM2 to CypB and PK, which contain BOX-I like motifs, was also demonstrated validating proteomic mass spectrometry screens as a method to identify new protein-protein interactions. To further investigate the potential of linear motifs to modulate protein-protein interactions, a peptide aptamer targeting the protein AGR2 was tested for effect on AGR2 and p53 in a cancer cell line.