Ryanodine receptor 1 (RyR1) is a homotetrameric calcium channel located in the sarcoplasmic reticulum (SR) of skeletal muscle. We employed metal affinity chromatography followed by liquid chromatography mass spectrometry from HEK-293 cells to purify affinity tagged cytosolic RyR1, with interacting proteins. In total, we identified 703 proteins with high confidence (>99%). Of the putative RyR1 interacting proteins, five candidates [calcium homeostasis endoplasmic reticulum protein (CHERP), ER-golgi intermediate compartment 53kDa protein (LMAN1), T-complex protein (TCP), phosphorylase b kinase (PHBK) and four and half LIM domains protein 1 (FHL1)], were selected for interaction studies. Immunofluorescence analysis showed that CHERP co-localizes with RyR1 in the SR of rat soleus muscle. Calcium transient assays in HEK293 cells over-expressing RyR1 with siRNA suppressed CHERP or FHL1, showed reduced calcium release via RyR1. In conclusion, we have identified RyR1 interacting proteins in CHERP and FHL1 which may represent novel regulatory mechanisms involved in excitation-contraction coupling.

Ryanodine receptor 1 (RyR1) is a homotetrameric calcium channel located in the sarcoplasmic reticulum (SR) of skeletal muscle. We employed metal affinity chromatography followed by liquid chromatography mass spectrometry from HEK-293 cells to purify affinity tagged cytosolic RyR1, with interacting proteins. In total, we identified 703 proteins with high confidence (>99%). Of the putative RyR1 interacting proteins, five candidates [calcium homeostasis endoplasmic reticulum protein (CHERP), ER-golgi intermediate compartment 53kDa protein (LMAN1), T-complex protein (TCP), phosphorylase b kinase (PHBK) and four and half LIM domains protein 1 (FHL1)], were selected for interaction studies. Immunofluorescence analysis showed that CHERP co-localizes with RyR1 in the SR of rat soleus muscle. Calcium transient assays in HEK293 cells over-expressing RyR1 with siRNA suppressed CHERP or FHL1, showed reduced calcium release via RyR1. In conclusion, we have identified RyR1 interacting proteins in CHERP and FHL1 which may represent novel regulatory mechanisms involved in excitation-contraction coupling.

Proteases are essential for regulating a wide range of physiological and pathological processes. The imbalance of protease activation and inhibition will result in a number of major diseases including cancers, atherosclerosis, and neurodegenerative diseases. Although fluorescence resonance energy transfer (FRET)-based protease probes, a small molecular dye and other methods are powerful, they still have drawbacks or limitations for providing significant information about the dynamics and pattern of endogenous protease activation and inhibition in a single living cell or in vivo. Currently protease sensors capable of quantitatively measuring specific protease activity in real time and monitoring activation and inhibition of enzymatic activity in various cellular compartments are highly desired. In this dissertation, we report a novel strategy to create protease sensors by grafting an enzymatic cleavage linker into a sensitive location for changing chromophore properties of enhanced green fluorescent protein (EGFP) following protease cleavage, which can be used to determine protease activity and track protease activation and inhibition with a ratiometric measurement mode in living cells. Our designed protease sensors exhibit large relative ratiometric optical signal change in both absorbance and fluorescence, and fast response to proteases. Meanwhile, these protease sensors exhibiting high enzymatic selectivity and kinetic responses are comparable or better than current small peptide probes and FRET-based protease probes. Additionally, our protease sensors can be utilized for real-time monitoring of cellular enzymogen activation and effects of inhibitors in living cells. This novel strategy opens a new avenue for developing specific protease sensors to investigate enzymatic activity in real time, to probe disease mechanisms corresponding to proteases in vitro and in vivo, and to screen protease inhibitors with therapeutic effects. Strong fluorescence was still retained in the cleaved EGFP-based protease sensors, which stimulated us to identify the EGFP fragment with fluorescence properties for further understanding chromophore formation mechanisms and investigating protein-protein interactions through fluorescence complementation of split EGFP fragments. Through fusing EF-hand motifs from calbindin D9k to split EGFP fragments, a novel molecular probe was developed to simultaneously track the calcium change or calcium signaling pathways and calcium-dependent protein-protein interaction in living cells in real time.

Calmodulin (CaM) is a ubiquitous cytosolic Ca2+-binding protein involved in the binding and regulation of more than three-hundred intracellular target proteins. CaM consists of two globular domains joined by a central linker region. In the archetypical model of CaM binding to a target protein, the Ca2+-replete CaM wraps its two domains around a single α-helical target peptide; however, other conformations of CaM bound to target peptides and proteins have recently been discovered. Due to its ability to bind and affect many different intracellular processes, there is significant interest in a better understanding of the structural and conformational basis of CaM’s ability to bind and recognize target proteins. The mammalian nitric oxide synthase (NOS) enzymes are bound and activated by CaM. The NOS enzymes catalyze the production of nitric oxide (•NO), a free radical involved in numerous intercellular processes such as neurotransmission, vasodilation, and immune defense. There are three different isoforms of nitric oxide synthase (NOS) found in mammals – neuronal NOS (nNOS), endothelial NOS (eNOS), and inducible NOS (iNOS). All three enzymes are homodimeric with each monomer consisting of an N-terminal oxygenase domain and a multidomain C-terminal reductase domain. A CaM-binding domain separates the oxygenase and reductase domains. There is a unique opportunity to investigate CaM’s control over •NO production by the NOS enzymes since each isoform shows a different mode of activation and control by CaM. At elevated cellular Ca2+ concentrations, CaM is able to bind and activate nNOS and eNOS. In contrast, the iNOS isozyme is transcriptionally regulated and binds to CaM in the absence of Ca2+. The focus of this thesis is to better our present understanding of the conformational and structural basis for CaM’s ability to bind and activate the three mammalian NOS isozymes with particular emphasis on the interactions between CaM and iNOS. To further investigate the differences in the association of CaM to the Ca2+-dependent and Ca2+-independent NOS isoforms, a variety of CaM mutants including CaM-troponin C chimeras, CaM EF hand pair proteins, and CaM mutants incapable of binding to Ca2+ were employed. The inherent differences in binding and activation observed using these CaM mutants is described. Differences in the binding of the N- and C-terminal domains, as well as the central linker of CaM to peptides corresponding to the CaM-binding domain of each NOS enzyme and holo-NOS enzymes was investigated. The conformation of CaM when bound to NOS peptides and holo-NOS enzymes was also studied using fluorescence (Förster) resonance energy transfer (FRET). A preliminary three-dimensional structural study of Ca2+-replete and Ca2+-deplete CaM in complex with an iNOS CaM-binding domain peptide is also described. Combining the cumulative results in this thesis, a working model for iNOS’s regulation by CaM is proposed. Future suggested experiments are described to further the characterization of CaM binding to the NOS enzymes and other CaM-target proteins. The studies described in this thesis have expanded and improved the present understanding of the CaM-dependent binding and activation of the NOS isozymes, particularly the interactions between CaM and iNOS.

The work describes the development of a new class of protein-inhibitors for protein-protein interactions, based on metallopeptides comprised of a dirhodium metal center. The metal incorporation in the peptide sequence leads to high increase in binding affinity of the inhibitors. The source of this strong affinity is the interaction of histidine on the protein surface with the rhodium center. In addition to this work, rhodium-based small molecule inhibitors for FK-506 binding proteins are investigated. Also, methodology for rhodium-catalyzed modification of proteins containing surface cysteine has been developed where a simple rhodium(II) complex catalyzes cysteine modification with diazo reagents. The reaction is marked by clean cysteine selectivity and mild reaction conditions. The resulting linkage is significantly more stable in human plasma serum, when compared to common maleimide reagents. Apart from this body of work in chemical-biology, the thesis contains the discussion of development of copper-catalyzed remote chlorination of alkyl hydroperoxides. The atom transfer chlorination utilizes simple ammonium chloride salts as the chlorine source and the internal redox process requires no external redox reagents.

Calmodulin (CaM) is a ubiquitous cytosolic Ca2+-binding protein involved in the binding and regulation of more than three-hundred intracellular target proteins. CaM consists of two globular domains joined by a central linker region. In the archetypical model of CaM binding to a target protein, the Ca2+-replete CaM wraps its two domains around a single α-helical target peptide; however, other conformations of CaM bound to target peptides and proteins have recently been discovered. Due to its ability to bind and affect many different intracellular processes, there is significant interest in a better understanding of the structural and conformational basis of CaM’s ability to bind and recognize target proteins. The mammalian nitric oxide synthase (NOS) enzymes are bound and activated by CaM. The NOS enzymes catalyze the production of nitric oxide (•NO), a free radical involved in numerous intercellular processes such as neurotransmission, vasodilation, and immune defense. There are three different isoforms of nitric oxide synthase (NOS) found in mammals – neuronal NOS (nNOS), endothelial NOS (eNOS), and inducible NOS (iNOS). All three enzymes are homodimeric with each monomer consisting of an N-terminal oxygenase domain and a multidomain C-terminal reductase domain. A CaM-binding domain separates the oxygenase and reductase domains. There is a unique opportunity to investigate CaM’s control over •NO production by the NOS enzymes since each isoform shows a different mode of activation and control by CaM. At elevated cellular Ca2+ concentrations, CaM is able to bind and activate nNOS and eNOS. In contrast, the iNOS isozyme is transcriptionally regulated and binds to CaM in the absence of Ca2+. The focus of this thesis is to better our present understanding of the conformational and structural basis for CaM’s ability to bind and activate the three mammalian NOS isozymes with particular emphasis on the interactions between CaM and iNOS. To further investigate the differences in the association of CaM to the Ca2+-dependent and Ca2+-independent NOS isoforms, a variety of CaM mutants including CaM-troponin C chimeras, CaM EF hand pair proteins, and CaM mutants incapable of binding to Ca2+ were employed. The inherent differences in binding and activation observed using these CaM mutants is described. Differences in the binding of the N- and C-terminal domains, as well as the central linker of CaM to peptides corresponding to the CaM-binding domain of each NOS enzyme and holo-NOS enzymes was investigated. The conformation of CaM when bound to NOS peptides and holo-NOS enzymes was also studied using fluorescence (Förster) resonance energy transfer (FRET). A preliminary three-dimensional structural study of Ca2+-replete and Ca2+-deplete CaM in complex with an iNOS CaM-binding domain peptide is also described. Combining the cumulative results in this thesis, a working model for iNOS’s regulation by CaM is proposed. Future suggested experiments are described to further the characterization of CaM binding to the NOS enzymes and other CaM-target proteins. The studies described in this thesis have expanded and improved the present understanding of the CaM-dependent binding and activation of the NOS isozymes, particularly the interactions between CaM and iNOS.

<p><p>One of the most significant discoveries from mutational analysis of protein interfaces is that often a large percentage of interface residues negligibly perturb the binding energy upon mutation, while residues in a few critical "hotspots" drastically reduce affinity when mutated. The organization of protein interfaces into hotspots has a number of important implications. For example, small interfaces can have high affinity, and when multiple binding partners are generated to the same protein, they are predisposed to binding the same regions and often have the same hotspots. Even small molecules that bind to interfaces and disrupt protein-protein interactions (PPIs) tend to bind at hotspots. This suggests that some hotspot-forming sites on protein surfaces are <italic>intrinsically</italic> more apt to form protein interfaces. These observations paint a hotspot-centric picture of PPI energetics, and present a question of fundamental importance which remains largely unanswered: <italic>why are hotspots hot?</italic></p></p><p><p>In order to gain insight into the nature of hotspots I experimentally examined the small, but high-affinity interface between the synthetically evolved ankyrin repeat protein Off7 with E. coli maltose binding protein by characterization of mutant variants and redesigned interfaces. In order to characterize many mutants, I developed two high-throughput assays to measure protein-protein binding that integrate with existing technology for the high-throughput fabrication of genes. The first is an ELISA-based method using in vitro expressed protein for semi-quantitative analysis of affinity. Starting from DNA encoding protein partners, binding data is obtained in just a few hours; no exogenous purification is required. For the second assay, I develop data fitting methods and thermodynamic framework for determination of binding free energies from binding-induced shifts in protein thermal stability monitored with Sypro Orange.</p></p><p><p>Analysis of Off7/MBP variants using these methods reveals that conservative mutagenesis or local computational repacking is tolerated for many residues in the interface without drastic loss of affinity, except for a single essential hotspot. This hotspot contains a Tyr-His-Asp hydrogen bonding network reminiscent of a common catalytic motif. Substitution of the tyrosine with phenylalanine shows that a single hydrogen bond across the interface is critical for binding. Analysis of the protein database by structural bioinformatics shows that, although rare, this motif is present in other naturally evolved interfaces. Such a triad was found in the homodimeric interface of PH0642 from Pyrococcus horikoshii, and is conserved between many homologues in the nitrilase superfamily, meeting one of the key criteria by which potential hotspots can be identified. This analysis supports a number of analogies between hotspot residues and catalytic residues in enzyme active sites, and raises the intriguing possibility that hotspots may be associated with other structural motifs that could be used for identification or design of PPIs.</p></p> / Dissertation

Prediction of protein-protein interactions is an important part in understanding the biological processes in a living cell. There are completely sequenced organisms that do not yet have experimentally verified protein-protein interaction networks. For such organisms, we can not generally use a supervised method, where a portion of the protein-protein interaction network is used as training set. Furthermore, for newly-sequenced organisms, many other data sources, such as gene expression data and gene ontology annotations, that are used to identify protein-protein interaction networks may not be available. In this thesis work, our aim is to identify and cluster likely protein-protein interaction pairs using only sequence of proteins and evolutionary information. We use a protein&rsquo / s phylogenetic profile because the co-evolutionary pressure hypothesis suggests that proteins with similar phylogenetic profiles are likely to interact. We also divide phylogenetic profile into smaller profiles based on the evolutionary lines. These divided profiles are then used to score the similarity between all possible protein pairs. Since not all profile groups have the same number of elements, it is a difficult task to assess the similarity between such pairs. We show that many commonly used measures do not work well and that the end result greatly depends on the type of the similarity measure used. We also introduce a novel similarity measure. The resulting dense putative interaction network contains many false-positive interactions, therefore we apply the Markov Clustering algorithm to cluster the protein-protein interaction network and filter out the weaker edges. The end result is a set of clusters where proteins within the clusters are likely to be functionally linked and to interact. While this method does not perform as well as supervised methods, it has the advantage of not requiring a training set and being able to work only using sequence data and evolutionary information. So it can be used as a first step in identifying protein-protein interactions in newly-sequenced organisms.

Protein-protein interaction networks provide important information about what the biological function of proteins whose roles are unknown might be in a cell. These interaction networks were analyzed by a variety of approaches by running them on a single computer and the roles of the proteins identified were used to predict the function of the proteins unidentified. The functional flow is an approach that takes the network connectivity, distance effect, topology of the network with local and global views into account. With these advantages, that the functional flow produces more accurate results on the prediction of protein functions was presented by the previos conducted researches. However, the application implemented for this approach could not be practically applied on the large and complex network produced for the complex species because of memory limitation. The purpose of this thesis is to provide a new application be implemented on the high computing performance where the application can be scaled on the large data sets. Therefore, Hadoop, one of the open source map/reduce environments, was installed on 18 hosts each of which has eight cores. Method / the first map/reduce job distributes the protein interaction network as a format which allows parallel distributed computing to all the worker nodes, the other map/reduce job generates flows for each known protein function and the role of the proteins unidentified are predicted by accumulating all of these generated flows. It has been observed in the experiments we performed that the application requiring high performance computing can be decomposed into worker nodes efficiently and the application can provide better performance as the resources increase.

Reconstructing the topology of a signaling network by means of RNA interference (RNAi) technology is an underdetermined problem especially when a single gene in the network is knocked down or observed. In addition, the exponential search space limits the existing methods to small signaling networks of size 10-15 genes. In this thesis, we propose integrating RNAi data with a reference physical interaction network. We formulate the problem of signaling network reconstruction as finding the minimum number of edit operations on a given reference network. The edit operations transform the reference network to a network that satisfy the RNAi observations. We show that using a reference network does not simplify the computational complexity of the problem. Therefore, we propose an approach that provides near optimal results and can scale well for reconstructing networks up to hundreds of components. We validate the proposed method on synthetic and real datasets. Comparison with the state of the art on real signaling networks shows that the proposed methodology can scale better and generates biologically significant results.