Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 87-94).

<p>Studying protein interaction networks using functional and topological information is important for understanding cellular organization and functionality. This study deals with identifying important proteins in protein interaction networks using SWEMODE (Lubovac, et al, 2006) and analyzing topological and functional properties of these proteins with the help of information derived from modular organization in protein interaction networks as well as information available in public resources, in this case, annotation sources describing the functionality of proteins. Multi-modular proteins are short-listed from the modules generated by SWEMODE. Properties of these short-listed proteins are then analyzed using functional information from SGD Gene Ontology(GO) (Dwight, et al., 2002) and MIPS functional categories (Ruepp, et al., 2004). Topological features such as lethality and centrality of these proteins are also investigated, using graph theoretic properties and information on lethal genes from Yeast Hub (Kei-Hoi, et al., 2005). The findings of the study based on GO terms reveal that these important proteins are mostly involved in the biological process of “organelle organization and biogenesis” and a majority of these proteins belong to MIPS “cellular organization” and “transcription” functional categories. A study of lethality reveals that multi-modular proteins are more likely to be lethal than proteins present only in a single module. An examination of centrality (degree of connectivity of proteins) in the network reveals that the ratio of number of important proteins to number of hubs at different hub sizes increases with the hub size (degree).</p>

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 139-147).

Abstract The function of the vertebrate nervous system is based on the rapid and accurate transmission of electrical impulses. The myelin sheath is a lipid-rich membrane that envelops the axon, preventing the leakage of the nervous impulse to the environment. Myelin is formed when the plasma membrane of a myelinating glial cell differentiates and wraps around an axon. The compaction of myelin leads to the extrusion of most of the glial cell cytoplasm from the structure. Both the compact and noncompact regions of myelin carry distinct subsets of proteins. The myelin-associated glycoprotein (MAG) is present in noncompact myelin. It is a cell adhesion molecule expressed only by myelinating glial cells. Two isoforms of MAG, S- and L-MAG, exist, and these forms differ from each other only by their cytoplasmic domains. Until now, little information has been available on the differences between the MAG isoforms. This study was carried out in order to gain information on the cytoplasmic domains of S- and L-MAG. Significant differences were observed in the properties of the MAG cytoplasmic domains. An interaction between the L-MAG cytoplasmic domain and the S100b protein was characterised, and a role for this interaction was found in the regulation of L-MAG phosphorylation. Evidence was also obtained for the dimerisation of the L-MAG cytoplasmic domain. The S-MAG cytoplasmic domain bound zinc, which induced a change in the surface properties of the protein. The S-MAG cytoplasmic domain was also found to interact directly with tubulin, the core component of microtubules. In conclusion, this study has brought information on the functions of the MAG cytoplasmic domains. The results are complementary with ealier hypotheses on the roles of the MAG isoforms in myelinating glia. While the properties of L-MAG suggest a role as a signaling molecule, a dynamic structural role for S-MAG during myelin formation and maintenance can be envisaged.

Protein-protein interactions modulate cellular processes in health and disease. However, tracing weak or rare associations or dissociations of proteins is not a trivial task. Kinases are often regulated through interaction partners and, at the same time, themselves regulate cellular interaction networks. The use of kinase domains for creating a synthetic sensor device that reads low concentration protein-protein interactions and amplifies them to a higher concentration interaction which is then translated into a FRET (Fluorescence Resonance Energy Transfer) signal is here proposed. To this end, DNA constructs for interaction amplification (split kinases), positive controls (intact kinase domains), scaffolding proteins and phosphopeptide - SH2-domain modules for the reading of kinase activity were assembled and expression protocols for fusion proteins containing Lyn, Src, and Fak kinase domains in bacterial and in cell-free systems were optimized. Also, two non-overlapping methods for measuring the kinase activity of these proteins were stablished and, finally, a protein-fragment complementation assay with the split-kinase constructs was tested. In conclusion, it has been demonstrated that features such as codon optimization, vector design and expression conditions have an impact on the expression yield and activity of kinase-based proteins. Furthermore, it has been found that the defined PURE cell-free system is insufficient for the active expression of catalytic kinase domains. In contrast, the bacterial co-expression with phosphatases produced active kinase fusion proteins for two out of the three tested Tyrosine kinase domains.

The mamallian target of rapamycin (mTOR) is a protein implicated in a variety of cellular processes involving growth and division. In the context of the brain, it regulates synaptic plasticity and axon elongation; its dysfunction is implicated in the pathogenesis of multiple complex, heterogeneous neurodegenerative diseases. These include, but are not limited to Alzheimer’s Disease (AD), autism spectrum disorder (ASD), and epilepsy. mTOR boasts a deeply complex and far-reaching signalling cascade, and its activity affects the expression levels of a large number of proteins. As such, investigation of the proteins with whom mTOR interacts is a pertinent endeavor to the advancement of understanding the complex pathogenesis of neurodegenerative disease. The complexity of this endeavor makes it a target well-poised for protein-protein interaction network (PPIN) analysis. Thus, using a previously recorded MS/MS dataset listing proteins whose expression levels change upon rapamycin administration, we set out to identify key proteins and characterize the properties of the mTOR interactome overall using a variety of toplogical measures and analytical techniques. Using such techniques, we found that the in the PPIN created from our data, a certain subset of proteins subjected the network to particular fragility. Namely, the kinless hubs, which have high within-module degree as well as a large participation coefficient, show vulnerability exceeding that of even conventionally defined hub. Some of these kinless hubs exhibit critical structural roles in the PPIN such that their removal damages the overall efficiency of communication within the network at an individually observable level. Work is ongoing to further investigate these proteins and the potential biological implications of their importance in the network described in the present study.

The rhomboid protease, Pcp1p, localizes to the mitochondrial inner membrane in Saccharomyces cerevisiae. The 346 amino acid protein contains an N-terminal mitochondria targeting signal followed by 6 transmembrane helices. Pcp1p substrates include, Ccp1p and Mgm1p; both soluble intermembrane space proteins after Pcp1p cleavage. Haploid pcp1 mutants grow slow, lose mitochondrial DNA, and have abnormal mitochondrial morphology; phenotypes similar to mgm1 mutants. Processing of Mgm1p depends on Pcp1p activity, matrix ATP levels, and a functional Tim23-Pam complex. This suggests a potential link between Pcp1p and the Tim23 complex. To test this hypothesis, epitope tagged versions of Pcp1p, Tim23p, Tim21p, and Pam18p were generated for use in immunoprecipitation and sucrose gradient ultracentrifugation experiments. The data suggest that Pcp1p exists in a higher order protein complex that may contain multiple Pcp1p subunits and components of the Tim23 translocon. The importance of this interaction in the processing of precursor proteins remains to be determined.

No description available.

Background: Genome-wide association studies (GWAS) prove to be a powerful approach to identify the genetic basis of various human[1] diseases. Here we take advantage of existing GWAS data and attempt to build a framework to understand the complex relationships among diseases. Specifically, we examined 49 diseases from all available GWAS with a cascade approach by exploiting network analysis to study the single nucleotide polymorphisms (SNP) effect on the similarity between different diseases. Proteins within perturbation subnetwork are considered to be connection points between the disease similarity networks. Results: shared disease subnetwork proteins are consistent, accurate and sensitive to measure genetic similarity between diseases. Clustering result shows the evidence of phenome similarity. Conclusion: our results prove the usefulness of genetic profiles for evaluating disease similarity and constructing disease relationship networks. / Master of Science

Proteins participate in the majority of cellular processes. To determine the function of a protein it is not sufficient to solely know its sequence, its structure in isolation, or how it works individually. Additionally, we need to know how the protein interacts with other proteins in biological networks. This is because most of the proteins perform their main function through interactions. This thesis sets out to improve the understanding of protein-protein interaction networks (PPINs). For this, we propose three approaches: (1) Studying measures and methods used in social and complex networks. The methods, measures, and properties of social networks allow us to gain an understanding of PPINs via the comparison of different types of network families. We studied models that describe social networks to see which models are useful in describing biological networks. We investigate the similarities and differences in terms of the network community profile and centrality measures. (2) Studying PPINs and their role in evolution. We are interested in the relationship of PPINs and the evolutionary changes between species. We investigate whether the centrality measures are correlated with the variability and similarity in orthologous proteins. (3) Studying protein features that are important to evaluate, classify, and predict interactions. Interactions can be classified according to different characteristics. One characteristic is the energy (that is the attraction or repulsion of the molecules) that occurs in interacting proteins. We identify which type of energy values contributes better to predicting PPIs. We argue that the number of energetic features and their contribution to the interactions can be a key factor in predicting transient and permanent interactions. Contributions of this thesis include: (1) We identified the best community sizes in PPINs. This finding will help to identify important groups of interacting proteins in order to better understand their particular interactions. We furthermore find that the generative model describing biological networks is very different from the model describing social networks A generative model is a model for randomly generating observable data. We showed that the best community size for PPINs is around ten, different from the best community size for social and complex network (around 100). We revealed differences in terms of the network community profile and correlations of centrality measures; (2) We outline a method to test correlation of centrality measures with the percentage of sequence similarity and evolutionary rate for orthologous proteins. We conjecture that a strong correlation exists. While not obtaining positive results for our data. Therefore, (3) we investigate a method to discriminate energetic features of protein interactions that in turn will improve the PPIN data. The use of multiple data sets makes possible to identify the energy values that are useful to classify interactions. For each data set, we performed Random Forest and Support Vector Machine with linear, polynomial, radial, and sigmoid kernels. The accuracy obtained in this analysis reinforces the idea that energetic features in the protein interface help to discriminate between transient and permanent interactions. / Graduate / 0984