Proteins can be described as the “machineries” responsible for almost all tasks in the levels of organizational complexity in multi-cellular organisms namely: the cells, tissues, organs and systems. Any disorder in the function of a protein at any of these levels could result in disease, and a study of protein function is critical to understanding the pathological features of the disease at the molecular level. A quick glance at these abundantly present proteins reveals two striking features: large diversity of biological function, and the variations in structural complexity, which varies from simple random coils, to turns and helices, and on to structured assemblies of turns, helices and sheets. Over the past few years, more research efforts have been channeled to the application of synthetic research to protein dynamics, most especially in disease conditions. This provides insight into the design and development of chemical tools capable of modulating protein functions .Some of such tools include small molecules, peptides and peptidomimetics. In this work, we have described the application of these tools to three (3) different disease systems topping the list of incurable diseases: HIV, Diabetes, and Cancer. We have designed and developed chemical probes to facilitate a better understanding of major “culprit” proteins underlying the pathological conditions associated with these diseases. Our designed chemical probes were capable of modulating protein functions by producing the desired effects: inhibition of protein-protein interactions.

Heme is the major iron source for the deadly human pathogen, Group A Streptococcus (GAS). During infection, GAS lyses host cells releasing hemoglobin and other hemoproteins. This dissertation aims to elucidate the general mechanism by which GAS obtains and utilizes heme as an iron source from the host hemoproteins. GAS encodes a heme relay system consisting of Shr, Shp and the SiaABC transporter. We specifically determine the role of Shr in the heme uptake process, by conducting a detailed functional characterization of its constituent domains. We also undertake to solve the long-standing mystery surrounding the catabolism of heme in streptococci. The studies presented herein established Shr as a prototype of a new family of NEAT-containing hemoproteins receptors. They demonstrate its importance in heme acquisition by GAS and provide a molecular model for heme scavenging and transfer by the protein. We show that Shr modulates heme uptake depending on heme availability by a mechanism where NEAT1 facilitates fast heme scavenging and delivery to Shp, whereas NEAT2 serves as a temporary storage for heme on the bacterial surface. Finally, we identified and characterized for the first time, a heme oxygenase (HO) in the Streptococcus genus which was named HupZ. Sequence comparison between HupZ and several HOs from different structural families indicates that this enzyme is unrelated to any of the previously characterized HOs. However, orthologs of the protein are found in other important pathogens. The structure and the catalytic mechanism of HupZ suggest that it is the representative of a new family of flavoenzymes capable of degrading heme using their reduced flavin cofactor as a source of electrons. Overall, this work contributes significant knowledge to the topic of heme utilization by pathogens and importantly, provides new direct evidence that associates flavins with heme metabolism in bacteria. Thus it sets a new direction in the field and lays the ground for future fundamental and applied discoveries.

Cancer is the main cause of death in economically developed countries and the second leading cause of death in developing countries. Along with today’s knowledge that more than two hundred different diseases lie in the category of this prognosis there is an urge for more detailed and case-specific treatments to replace the dramatic actions of available radiation- and chemotherapy, which in many cases do not make a difference between healthy and cancer cells. The transcription factor and onco-protein c-Myc has, after being extensively studied during the past decades, become a prognostic marker for almost all cancer forms known. Still, many questions remain regarding how c-Myc interacts with its many different target proteins involved in cell-cycle regulation, proliferation and apoptosis. Current cell biology states that one of the regulating proteins, hPin1, interacts with c-Myc in a phosphorylation-dependent manner which appears to direct the correct timing of c-Myc activation and degradation through the ubiquitin/proteasome-pathway. The critical phosphorylation sites, T58 and S62, are located in the Myc-Box-I (MBI) region, a highly conserved sequence strongly coupled to aggressive tumourigenesis by hotspot mutations. Interestingly, preliminary results in the Sunnerhagen group suggested that MBI alone did not bind hPin1, suggesting hPin1 targeting a site distal from the residues to be phosphorylated. In this thesis, results from Surface Plasmon Resonance (SPR) and Nuclear Magnetic Resonance (NMR) show that the docking WW-domain of hPin1 binds unphosphorylated c-Myc at a region distal from the phosphorylation site, including residues 13-34. Furthermore, SPR experiments revealed that hPin1 binds unphosphorylated c-Myc with apparently greater affinity and with much slower kinetics than phosphorylated c-Myc. Thus, hPin1 recognition and interaction with c-Myc appears not to be dependent on phosphorylation of c-Myc prior binding. The newly identified binding region of c-Myc, located N-terminal of MBI, may further increase the understanding of protein degradation control and c-Myc function. The studies presented in this thesis provide a brick in the puzzle of c-Myc and hPin1 coupled oncogenesis for further development of new therapeutic strategies.

The type II secretion system is used by many pathogenic and non-pathogenic bacteria for the extracellular secretion of enzymes and toxins. <i>Aeromonas hydrophila</i> is a Gram-negative pathogen that secretes proteins via the type II secretion system.<p> In the studies described here, a series of yeast two-hybrid assays was performed to identify protein-protein interactions in the type II secretion system of <i>A. hydrophila</i>. The periplasmic domains of ExeA and ExeB were assayed for interactions with the periplasmic domains of Exe A, B, C, D, K, L, M, and N. Interactions were observed for both ExeA and ExeB with the secretin ExeD in one orientation. In addition, a previously identified interaction between ExeC and ExeD was observed. In order to further examine and map these interactions, a series of eight two-codon insertion mutations in the amino terminal domain of ExeD was screened against the periplasmic domains of ExeA and ExeB. As a result, the interactions were verified and mapped to subdomains of the ExeD periplasmic domain. To positively identify the region of ExeD involved in the interactions with ExeA, B, C and D, deletion mutants of ExeD were constructed based on the two-codon insertion mutation mapping of subdomains of the ExeD periplasmic domain, and yeast two-hybrid assays were carried out. The results showed that a fragment of the periplasmic domain of ExeD, from amino acid residue 26 to 200 of ExeD, was involved in the interactions with ExeA, B and C. As an independent assay for interactions between ExeAB and the secretin, His-tagged derivatives of the periplasmic domains of ExeA and ExeB were constructed and co-purification on Ni-NTA agarose columns was used to test for interactions with untagged ExeD. These experiments confirmed the interaction between ExeA and ExeD, although there was background in the co-purification test.<p> These results provide support for the hypothesis that the ExeAB complex functions to organize the assembly of the secretin through interactions between both peptidoglycan and the secretin that result in its multimerization into the peptidoglycan and outer membrane layers of the envelope.

Metabolic networks are the collections of all cellular activities taking place in a living cell and all the relationships among biological elements of the cell including genes, proteins, enzymes, metabolites, and reactions. They provide a better understanding of cellular mechanisms and phenotypic characteristics of the studied organism. In order to reconstruct a metabolic network, interactions among genes and their molecular attributes along with their functions must be known. Using this information, proteins are distributed among pathways as sub-networks of a greater metabolic network. Proteins which carry out various steps of a biological process operate in same pathway.<p>The metabolic network of <i>Caenorhabditis elegans</i> was reconstructed based on current genomic information obtained from the KEGG database, and commonly found in SWISS-PROT and WormBase. Assuming proteins operating in a pathway are interacting proteins, currently available protein-protein interaction map of the studied organism was assembled. This map contains all known protein-protein interactions collected from various sources up to the time. Topology of the reconstructed network was briefly studied and the role of key enzymes in the interconnectivity of the network was analysed. The analysis showed that the shortest metabolic paths represent the most probable routes taken by the organism where endogenous sources of nutrient are available to the organism. Nonetheless, there are alternate paths to allow the organism to survive under extraneous variations. <p>Signature content information of proteins was utilized to reveal protein interactions upon a notion that when two proteins share signature(s) in their primary structures, the two proteins are more likely to interact. The signature content of proteins was used to measure the extent of similarity between pairs of proteins based on binary similarity score. Pairs of proteins with a binary similarity score greater than a threshold corresponding to confidence level 95% were predicted as interacting proteins. The reliability of predicted pairs was statistically analyzed. The sensitivity and specificity analysis showed that the proposed approach outperformed maximum likelihood estimation (MLE) approach with a 22% increase in area under curve of receiving operator characteristic (ROC) when they were applied to the same datasets. When proteins containing one and two known signatures were removed from the protein dataset, the area under curve (AUC) increased from 0.549 to 0.584 and 0.655, respectively. Increase in the AUC indicates that proteins with one or two known signatures do not provide sufficient information to predict robust protein-protein interactions. Moreover, it demonstrates that when proteins with more known signatures are used in signature profiling methods the overlap with experimental findings will increase resulting in higher true positive rate and eventually greater AUC. <p>Despite the accuracy of protein-protein interaction methods proposed here and elsewhere, they often predict true positive interactions along with numerous false positive interactions. A global algorithm was also proposed to reduce the number of false positive predicted protein interacting pairs. This algorithm relies on gene ontology (GO) annotations of proteins involved in predicted interactions. A dataset of experimentally confirmed protein pair interactions and their GO annotations was used as a training set to train keywords which were able to recover both their source interactions (training set) and predicted interactions in other datasets (test sets). These keywords along with the cellular component annotation of proteins were employed to set a pair of rules that were to be satisfied by any predicted pair of interacting proteins. When this algorithm was applied to four predicted datasets obtained using phylogenetic profiles, gene expression patterns, chance co-occurrence distribution coefficient, and maximum likelihood estimation for S. cerevisiae and <i>C. elegans</i>, the improvement in true positive fractions of the datasets was observed in a magnitude of 2-fold to 10-fold depending on the computational method used to create the dataset and the available information on the organism of interest. <p>The predicted protein-protein interactions were incorporated into the prior reconstructed metabolic network of <i>C. elegans</i>, resulting in 1024 new interactions among 94 metabolic pathways. In each of 1024 new interactions one unknown protein was interacting with a known partner found in the reconstructed metabolic network. Unknown proteins were characterized based on the involvement of their known partners. Based on the binary similarity scores, the function of an uncharacterized protein in an interacting pair was defined according to its known counterpart whose function was already specified. With the incorporation of new predicted interactions to the metabolic network, an expanded version of that network was resulted with 27% increase in the number of known proteins involved in metabolism. Connectivity of proteins in protein-protein interaction map changed from 42 to 34 due to the increase in the number of characterized proteins in the network.

How tumor cells migrate and metastasize from primary sites requires four major steps: invasion, intravasation, extravasation and proliferation from micrometastases to malignant tumor. The initiation of tumor cell invasion requires Epithelial-Mesenchymal Transition (EMT), by which tumor cells lose cell-cell interactions and gain the ability of migration. The gene expression profile during the EMT process has been extensively investigated to study the initiation of EMT. In our studies, we indicated that tyrosine phosphorylation of human p68 RNA helicase positively associated with the malignant status of tumor tissue or cells. Studying of this relationship revealed that p68 RNA helicase played a critical role in EMT progression by repression of E-cadherin as an epithelial marker and upregulation of Vimentin as a mesenchymal marker. Insight into the mechanism of how p68 RNA helicase represses E-cadherin expression indicated that p68 RNA helicase initiated EMT by transcriptional upregulation of Snail. Human p68 RNA helicase has been documented as an RNA-dependent ATPase. The protein is an essential factor in the pre-mRNA splicing procedure. Some examples show that p68 RNA helicase functions as a transcriptional coactivator in ATPase dependent or independent manner. Here we indicated that p68 RNA helicase unwound protein complexes to modulate protein-protein interactions by using protein-dependent ATPase activity. The phosphorylated p68 RNA helicase displaced HDAC1 from the chromatin remodeling MBD3:Mi2/NuRD complex at the Snail promoter. Thus, our data demonstrated an example of protein-dependent ATPase which modulates protein-protein interactions within the chromatin remodeling machine.

<p>The cell cycle is one of the fundamental processes in all living organisms, and all cells arise from the division of existing cells. To better understand the regulation of the cell cycle, synchrony experiments are widely used to monitor cellular dynamics during this process. In such experiments, a large population of cells is generally arrested or selected at one stage of the cycle, and then released to progress through subsequent division stages. Measurements are then taken in this population at a variety of time points after release to provide insight into the dynamics of the cell cycle. However, due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, the time-series measurements from the synchronized cell populations do not accurately reflect the underlying dynamics of cell-cycle processes.</p><p>In this thesis, we introduce a deconvolution algorithm that learns a more accurate view of cell-cycle dynamics, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our method sharpens signal without sharpening noise, and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Though it can be applied to any such data, we demonstrate the utility of our method by applying it to a recent cell-cycle transcription time course in the eukaryote <italic>Saccharomyces cerevisiae</italic>. We show that our method more sensitively detects cell-cycle-regulated transcription, and reveals subtle timing differences that are masked in the original population measurements. Our algorithm also explicitly learns distinct transcription programs for both mother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in the early G1 in a daughter-specific manner.</p><p>In addition to the cell-cycle deconvolution algorithm, we introduce <italic>DOMAIN</italic>, a protein-protein interaction (PPI) network alignment method, which employs a novel <italic>direct-edge-alignment</italic> paradigm to detect conserved functional modules (e.g., protein complexes, molecular pathways) from pairwise PPI networks. By applying our approach to detect protein complexes conserved in yeast-fly and yeast-worm PPI networks, we show that our approach outperforms two widely used approaches in most alignment performance metrics. We also show that our approach enables us to identify conserved cell-cycle-related functional modules across yeast-fly PPI networks.</p> / Dissertation

Many pathological processes are associated with excessive neurotransmitter release that leads to the over-stimulation of post-synaptic neurotransmitter receptors. Examples include excessive activation of glutamate receptors in ischemic stroke and hyper-dopaminergic state in schizophrenia and drug addiction. Thus, it would seem that simply antagonizing the involved receptors should be able to correct the pathological condition. In some instances, this strategy has been somewhat effective, such as with the use of dopamine D2 receptor antagonists as antipsychotics in the treatment of positive symptoms of schizophrenia despite severe side effect. However, clinical application of drugs antagonizing glutamate receptor in the treatment of stoke, although attracting intensive research effort, has been restricted by serious side effects caused by suppressing postsynaptic responses that are needed for normal brain function. As a consequence, it is important to develop novel therapeutics aiming at specific targets with minimized side effects. Numerous studies have suggested that the pathophysiology of neuropsychiatric disorders, drug addictions and stroke involves multiple neurotransmitter receptor systems such as the dopamine and glutamate systems. The activation or inhibition of one receptor can have cross-functional effect that will be better understood by investigating the functional and structural relationship between receptor systems. Thus, the present study has focused on characterizing receptor-receptor interactions associated with dopamine receptors and glutamate receptors, and to elucidate the physiological and pathological consequence of altered receptor interactions in schizophrenia, depression and ischemic stroke.

Many pathological processes are associated with excessive neurotransmitter release that leads to the over-stimulation of post-synaptic neurotransmitter receptors. Examples include excessive activation of glutamate receptors in ischemic stroke and hyper-dopaminergic state in schizophrenia and drug addiction. Thus, it would seem that simply antagonizing the involved receptors should be able to correct the pathological condition. In some instances, this strategy has been somewhat effective, such as with the use of dopamine D2 receptor antagonists as antipsychotics in the treatment of positive symptoms of schizophrenia despite severe side effect. However, clinical application of drugs antagonizing glutamate receptor in the treatment of stoke, although attracting intensive research effort, has been restricted by serious side effects caused by suppressing postsynaptic responses that are needed for normal brain function. As a consequence, it is important to develop novel therapeutics aiming at specific targets with minimized side effects. Numerous studies have suggested that the pathophysiology of neuropsychiatric disorders, drug addictions and stroke involves multiple neurotransmitter receptor systems such as the dopamine and glutamate systems. The activation or inhibition of one receptor can have cross-functional effect that will be better understood by investigating the functional and structural relationship between receptor systems. Thus, the present study has focused on characterizing receptor-receptor interactions associated with dopamine receptors and glutamate receptors, and to elucidate the physiological and pathological consequence of altered receptor interactions in schizophrenia, depression and ischemic stroke.

Metabolic networks are the collections of all cellular activities taking place in a living cell and all the relationships among biological elements of the cell including genes, proteins, enzymes, metabolites, and reactions. They provide a better understanding of cellular mechanisms and phenotypic characteristics of the studied organism. In order to reconstruct a metabolic network, interactions among genes and their molecular attributes along with their functions must be known. Using this information, proteins are distributed among pathways as sub-networks of a greater metabolic network. Proteins which carry out various steps of a biological process operate in same pathway.<p>The metabolic network of <i>Caenorhabditis elegans</i> was reconstructed based on current genomic information obtained from the KEGG database, and commonly found in SWISS-PROT and WormBase. Assuming proteins operating in a pathway are interacting proteins, currently available protein-protein interaction map of the studied organism was assembled. This map contains all known protein-protein interactions collected from various sources up to the time. Topology of the reconstructed network was briefly studied and the role of key enzymes in the interconnectivity of the network was analysed. The analysis showed that the shortest metabolic paths represent the most probable routes taken by the organism where endogenous sources of nutrient are available to the organism. Nonetheless, there are alternate paths to allow the organism to survive under extraneous variations. <p>Signature content information of proteins was utilized to reveal protein interactions upon a notion that when two proteins share signature(s) in their primary structures, the two proteins are more likely to interact. The signature content of proteins was used to measure the extent of similarity between pairs of proteins based on binary similarity score. Pairs of proteins with a binary similarity score greater than a threshold corresponding to confidence level 95% were predicted as interacting proteins. The reliability of predicted pairs was statistically analyzed. The sensitivity and specificity analysis showed that the proposed approach outperformed maximum likelihood estimation (MLE) approach with a 22% increase in area under curve of receiving operator characteristic (ROC) when they were applied to the same datasets. When proteins containing one and two known signatures were removed from the protein dataset, the area under curve (AUC) increased from 0.549 to 0.584 and 0.655, respectively. Increase in the AUC indicates that proteins with one or two known signatures do not provide sufficient information to predict robust protein-protein interactions. Moreover, it demonstrates that when proteins with more known signatures are used in signature profiling methods the overlap with experimental findings will increase resulting in higher true positive rate and eventually greater AUC. <p>Despite the accuracy of protein-protein interaction methods proposed here and elsewhere, they often predict true positive interactions along with numerous false positive interactions. A global algorithm was also proposed to reduce the number of false positive predicted protein interacting pairs. This algorithm relies on gene ontology (GO) annotations of proteins involved in predicted interactions. A dataset of experimentally confirmed protein pair interactions and their GO annotations was used as a training set to train keywords which were able to recover both their source interactions (training set) and predicted interactions in other datasets (test sets). These keywords along with the cellular component annotation of proteins were employed to set a pair of rules that were to be satisfied by any predicted pair of interacting proteins. When this algorithm was applied to four predicted datasets obtained using phylogenetic profiles, gene expression patterns, chance co-occurrence distribution coefficient, and maximum likelihood estimation for S. cerevisiae and <i>C. elegans</i>, the improvement in true positive fractions of the datasets was observed in a magnitude of 2-fold to 10-fold depending on the computational method used to create the dataset and the available information on the organism of interest. <p>The predicted protein-protein interactions were incorporated into the prior reconstructed metabolic network of <i>C. elegans</i>, resulting in 1024 new interactions among 94 metabolic pathways. In each of 1024 new interactions one unknown protein was interacting with a known partner found in the reconstructed metabolic network. Unknown proteins were characterized based on the involvement of their known partners. Based on the binary similarity scores, the function of an uncharacterized protein in an interacting pair was defined according to its known counterpart whose function was already specified. With the incorporation of new predicted interactions to the metabolic network, an expanded version of that network was resulted with 27% increase in the number of known proteins involved in metabolism. Connectivity of proteins in protein-protein interaction map changed from 42 to 34 due to the increase in the number of characterized proteins in the network.