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.

This thesis documents the subcellular localization and protein-protein interactions of two methyl recycling enzymes. These two enzymes, adenosine kinase (ADK) and S-adenosyl-L-homocysteine hydrolase (SAHH), are essential to sustain the hundreds of S-adenosyl-L-methionine (SAM)-dependent transmethylation reactions in plants. Both ADK and SAHH are involved in the removal of a competitive inhibitor of methyltransferases (MTs), S-adenosyl-L-homocysteine (SAH), that is generated as a by-product of the each transfer of a methyl group from SAM to a substrate. This research focused on understanding how SAH is metabolized in distinct cellular compartments to maintain MT activities required for plant growth and development. Localization studies using green fluorescent protein (GFP) fusions revealed that both ADK and SAHH localize to the cytoplasm and the nucleus, and possibly to the chloroplast, despite the fact that the primary amino acid sequence of neither protein contains detectable targeting signals. This suggested the possibility that these methyl-recycling enzymes may be targeted by specific protein-protein interactions. Moreover, deletion analysis of SAHH1 indicated that the insertion region (IR) of 41 amino acids (Gly150-Lys190), which is present only in plants and parasitic protozoan SAHHs among eukaryotes, is essential for nuclear targeting. This result suggested that the surface-exposed IR loop may serve as a binding domain for interactions with other proteins that may direct SAHH to the nucleus. To investigate protein-protein interactions, several methods were performed including co-immunoprecipitation, bimolecular fluorescence complementation, and pull-down assays. These results not only revealed that ADK and SAHH possibly interact through the IR loop of SAHH in planta, but also suggested that this interaction is either dynamic or indirect, requiring a cofactor/another protein(s) or post-translational modifications. Moreover, possible interactions of both ADK and SAHH with a putative Arabidopsis mRNA cap methyltransferase (CMT), which is localized predominantly in the nucleus, were also confirmed. These results support the hypothesis that the nuclear targeting of both SAHH and ADK can be mediated by the interaction with CMT. In addition, purification of Strep-tagged SAHH1 expressed in Arabidopsis identified a novel interaction between SAHH and aspartate-semialdehyde dehydrogenase (ASDH), an enzyme that catalyzes the second step of the aspartate-derived amino acid biosynthesis pathway. Analysis of ASDH-GFP fusions revealed that ASDH localizes to the chloroplast and the stromule-like structure that emanates from chloroplasts. Moreover the mutation in three amino acids (Pro164-Asp165-Pro166) located within the IR loop of SAHH disrupted its binding to ASDH which affected the plastid localization of SAHH, suggesting that the interaction between SAHH and ASDH is required for plastid-targeting of SAHH. Taken together, this thesis demonstrated that the localization of ADK and SAHH in or between compartments is possibly mediated by specific protein interactions, and that the surface-exposed IR loop of SAHH is crucial for these interactions.

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.

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.

Essential proteins affect the cellular life deeply, but it is hard to identify them. Protein-protein interaction is one of the ways to disclose whether a protein is essential or not. We notice that many researchers use the feature set composed of topology properties from protein-protein interaction to predict the essential proteins. However, the functionality of a protein is also a clue to determine its essentiality. In this thesis, to build SVM models for predicting the essential proteins, our feature set contains the sequence properties which can influence the protein function, topology properties and protein properties. In our experiments, we download Scere20070107, which contains 4873 proteins and 17166 interactions, from DIP database. The ratio of essential proteins to nonessential proteins is nearly 1:4, so it is imbalanced. In the imbalanced dataset, the best values of F-measure, MCC, AIC and BIC of our models are 0.5197, 0.4671, 0.2428 and 0.2543, respectively. We build another balanced dataset with ratio 1:1. For balanced dataset, the best values of F-measure, MCC, AIC and BIC of our models are 0.7742, 0.5484, 0.3603 and 0.3828, respectively. Our results are superior to all previous results with various measurements.

Tumor susceptibility gene 101¡]TSG101¡^encodes an inactive ubiquitin conjugating E2 enzyme implicated in regulation of protein sorting, vesicular trafficking, transcription activation of nuclear receptor, cell growth and differentiation. Previous studies showed that TSG101 can be mono- or poly- ubiquitinated, which is relevant to its functional status. There are seven Lysine (K) sites, K6, K11, K27, K29, K33, K48 and K63, on ubiquitin (Ub). Polyubiquitination using different Ub K sites confers differential function for protein degradation, DNA damage repair, endocytosis and protein sorting. AIP4 E3 ubiquitin ligase modifies its substrates involved in erythroid and lymphoid lineage differentiation and the associated immune responses. Mutation in AIP4 gene resolves in multisystemic autoimmune disease. TSG101 was recently shown to be a molecular checkpoint for T cell receptor downregulation. Here we investigate the ubiqutination status of TSG101. The ubiquitin-conjugated protein in lysate of cells co-transfected with pHA-TSG101 and His-tagged wild type Ub or each K site mutant ubiquitin expression plasmids was purified on nickel beads and then subjected to western blotting using antibodies against HA-TSG101 or His-tag. The results showed that K series mutant had differential effect on the steady-state of HA-TSG101. Proteasome inhibitor could alleviate its degradation especially in the K63 ubiquitin expression group, implying K63 ubiquitination E3 ligase is critical in maintaining HA-TSG101 level. Our coimmunoprecipitation result demonstrated the interaction between AIP4 and HA-TSG101, implying that TSG101 might be a substrate for AIP4. The ectopic overexpression of AIP4 increased the amount of HA-TSG101 in an E3 ligase activity depended manner. Taken together, these results indicated that AIP4 activity mediating Ub K63 modification might be critical for regulating cellular TSG101 protein level. Further experiment should clarify this issue.

New methodologies for the efficient syntheses of 4,4-difluoro-4-bora-3a,4adiaza- s-indacenes (BODIPYs) and rosamines were developed. A serendipitous discovery led to a new reaction which afforded BODIPYs in high yields. Systematic studies of the kinetics and mechanisms of the new reaction were performed. A series of BODIPYs were successfully prepared using the new approach. A simple and efficient synthesis of rosamines with cyclic-amine substituents was devised. These new rosamines showed interesting anti-tumor activities. Several types of novel fluorescent compounds were prepared. Highly fluorescent GFP-chromophore analogs were designed and synthesized. The correlation between the optical properties and the structures was investigated. New pyronin dyes with mesoheteroatom substituents were efficiently prepared. The fluorescence properties of these compounds were highly dependent on the nature of the meso-substituents. A set of BODIPY dyes that fluoresce brightly above 600 nm were made. They were then used as acceptors to prepare water-soluble through-bond energy transfer cassettes. All the cassettes had complete energy transfer and high quantum yields in MeOH. A few also had good fluorescence properties in aqueous media and even on proteins. The through-bond energy transfer cassettes were used to monitor protein-protein interactions. In order to test our hypothesis, an artificial protein interaction system was built by utilizing the biotin/(strept)avidin interactions. Thus Atto425-BSA-biotin, streptavidin-cassette1 and avidin-cassette2 were prepared. The interactions between Atto425-BSA-biotin and cassette labeled (strept)avidin were successfully detected in vitro and in living cells by fluorescence techniques.

Finding reliable gene markers for accurate disease classification is very challenging due to a number of reasons, including the small sample size of typical clinical data, high noise in gene expression measurements, and the heterogeneity across patients. In fact, gene markers identified in independent studies often do not coincide with each other, suggesting that many of the predicted markers may have no biological significance and may be simply artifacts of the analyzed dataset. To nd more reliable and reproducible diagnostic markers, several studies proposed to analyze the gene expression data at the level of groups of functionally related genes, such as pathways. Given a set of known pathways, these methods estimate the activity level of each pathway by summarizing the expression values of its member genes and using the pathway activities for classification. One practical problem of the pathway-based approach is the limited coverage of genes by currently known pathways. As a result, potentially important genes that play critical roles in cancer development may be excluded. In this thesis, we first propose a probabilistic model to infer pathway/subnetwork activities. After that, we developed a novel method for identifying reliable subnetwork markers in a human protein-protein interaction (PPI) network based on probabilistic inference of subnetwork activities. We tested the proposed methods based on two independent breast cancer datasets. The proposed method can efficiently find reliable subnetwork markers that outperform the gene-based and pathway-based markers in terms of discriminative power, reproducibility and classification performance. The identified subnetwork markers are highly enriched in common GO terms, and they can more accurately classify breast cancer metastasis compared to markers found by a previous method.

Protein-protein interactions are important for the prediction of protein functions since two interacting proteins usually have similar functions in a cell. Available protein interaction networks are incomplete / but, they can be used to predict new interactions in a supervised learning framework. However, in the case that the known protein network includes large number of protein pairs, the training time of the machine learning algorithm becomes quite long. In this thesis work, our aim is to predict protein-protein interactions with a known portion of the interaction network. We used Support Vector Machines (SVM) as the machine learning algoritm and used the already known protein pairs in the network. We chose to use phylogenetic profiles of proteins to form the feature vectors required for the learner since the similarity of two proteins in evolution gives a reasonable rating about whether the two proteins interact or not. For large data sets, the training time of SVM becomes quite long, therefore we reduced the data size in a sensible way while we keep approximately the same prediction accuracy. We applied a number of clustering techniques to extract the most representative data and features in a two categorical framework. Knowing that the training data set is a two dimensional matrix, we applied data reduction methods in both dimensions, i.e., both in data size and in feature vector size. We observed that the data clustered by the k-means clustering technique gave superior results in prediction accuracies compared to another data clustering algorithm which was also developed for reducing data size for SVM training. Still the true positive and false positive rates (TPR-FPR) of the training data sets constructed by the two clustering methods did not give satisfying results about which method outperforms the other. On the other hand, we applied feature selection methods on the feature vectors of training data by selecting the most representative features in biological and in statistical meaning. We used phylogenetic tree of organisms to identify the organisms which are evolutionarily significant. Additionally we applied Fisher&sbquo / &Auml / &ocirc / s test method to select the features which are most representative statistically. The accuracy and TPR-FPR values obtained by feature selection methods could not provide to make a certain decision on the performance comparisons. However it can be mentioned that phylogenetic tree method resulted in acceptable prediction values when compared to Fisher&sbquo / &Auml / &ocirc / s test.

Genome scale protein-protein interactions (PPIs) are interpreted as networks or graphs with thousands of nodes from the perspective of computer science. PPI networks represent various types of possible interactions among proteins or genes of a genome. PPI data is vital in protein function prediction since functions of the cells are performed by groups of proteins interacting with each other and main complexes of the cell are made of proteins interacting with each other. Recent increase in protein interaction prediction techniques have made great amount of protein-protein interaction data available for genomes. As a consequence, a systematic visualization and analysis technique has become crucial. To the best of our knowledge, no PPI visualization tool consider multi-resolution viewing of PPI network. In this thesis, we implemented a new approach for PPI network visualization which supports multi-resolution viewing of compound graphs. We construct compound nodes and label them by using gene set enrichment methods based on Gene Ontology annotations. This thesis further suggests new methods for PPI network visualization.