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.

No description available.

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.

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

Restriction modification (RM) systems play a crucial role in preventing the entry of foreign DNA into the bacterial cell. The best studied Type I RM system is EcoKI from Escherichia coli K12. Both bacteriophage and conjugative plasmids have developed a variety of strategies to circumvent the host RM system. One such strategy involves the production of antirestriction proteins that mimic a short segment of DNA and efficiently inhibit the RM system. The main aim of this project was to analyse the interaction of EcoKI and its cognate methylase (MTase) with the T7 antirestriction protein, known as overcome classical restriction (Ocr), and various ArdA antirestriction proteins. Currently, there is a paucity of structural data on the complex formed between the Type I system and the antirestriction proteins. The aim of this work was twofold; (i) compare the interaction of MTase with DNA and Ocr and (ii) quantify the strength of interaction between MTase and various ArdA proteins. The MTase was fused to the Green Fluorescent Protein (GFP) to facilitate determination of the orientation of interaction with DNA and Ocr. Time resolved fluorescence measurements were carried out using the GFP-MTase fusion to determine the fluorescence lifetime and anisotropy decay. These experiments were conducted using a time resolved fluorescence instrument fabricated in-house. The values determined in these experiments were then used to perform fluorescence resonance energy transfer (FRET) measurements with fluorescently labelled DNA or Ocr. These measurements gave information concerning the relative orientation of the MTase with either DNA or Ocr. The GFP-MTase fusion was also used to quantify the strength of interaction with various ArdA proteins. Previous attempts to determine the strength of interaction between MTase and ArdA proteins by employing conventional techniques have been unsuccessful. Therefore, a novel method was developed that exploits the interaction of MTase with a cation exchange medium, which can subsequently be displaced upon binding to ArdA. This method facilitated the determination, for the first time, of a set of binding affinities for the MTase and ArdA interaction.

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

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.

The ability of proteins to bind other molecules in response to various stimuli in their microenvironment serves as a platform for extensive regulatory networks coordinating downstream cell actions. The correct function of these signaling pathways depends mostly on noncovalent interactions often affecting the structure of proteins and protein complexes. Understanding the molecular mechanism of a protein function in cell signaling therefore often depends on our knowledge of a three-dimensional structure. In this doctoral thesis, I present the work that led to the understanding of several protein-protein and protein-ligand interactions implicated in cell signaling at the molecular level. I applied nuclear magnetic resonance spectroscopy, small angle X-ray scattering and other biophysical methods to determine the molecular basis of inhibition of four signaling proteins: Calcium/Calmodulin (Ca2+ /CaM)-dependent protein kinase kinase 2 (CaMKK2); protease Caspase-2; Forkhead transcription factor FOXO3, and Apoptosis signal-regulating protein kinase 1 (ASK1). In particular, I investigated the distinct roles of 14-3-3 and Ca2+ /CaM in the regulation of CaMKK2 activity. I also studied in detail the mechanism how 14-3-3 interferes with the caspase-2 oligomerization and its nuclear localization as well as...

The synthesis of iron-sulfur clusters is an essential cellular process, which depends on complex biosynthetic pathways. In model eukaryotes, these pathways are the ISC pathway in the mitochondria and the CIA pathway in the cytosol. A recent genome and transcriptome analysis showed, that an amitochondriate protist Monocercomonoides exilis lacks the canonical ISC pathway, which has been replaced by a bacterial SUF pathway. A close free-living relative of M. exilis, Paratrimastix pyriformis possesses a mitochondrion-related organelle, yet also possesses a SUF pathway instead of ISC. The acquisition of the SUF pathway has been suggested as the primordial cause for mitochondrial loss in M. exilis, which is the first documented eukaryotic organism without a mitochondrion. The SUF pathway has been the subject of numerous studies in bacteria, however, its role as the core provider of iron-sulfur clusters for eukaryotic cells has been reported in merely a handful of eukaryotes and was based predominantly on genomic data. This thesis focuses on the putative ATPase SufC and the putative scaffold protein SufB. Both proteins were successfully produced in recombinant forms. SufC has been found to possess ATPase activity in vitro, which was increased upon interaction with SufB. The conditions for theATPase...

Proteins and their function play one of the most essential roles in various biological processes. The study of PPI is of considerable importance. PPI network data are of great scientific value, however, they are incomplete and experimental identification is time and money consuming. Available computational methods perform well on model organisms’ PPI prediction but perform poorly for a novel organism. Due to the incompleteness of interaction data, it is challenging to train a model for a novel organism. Also, millions to billions of interactions need to be verified which is extremely compute-intensive. We aim to improve the performance of predicting whether a pair of proteins will interact, with only two sequences as input. And also efficiently predict a PPI network with a proteome of sequences as input. We hypothesize that information about cellular locations where proteins are active and proteins' 3D structures can help us to significantly improve predict performance. To overcome the lack of experimental data, we use predicted structures by AlphaFold2 and cellular locations by DeepGoPlus. We believe that proteins belonging to disjoint biological components have very little chance to interact. We manually choose several disjoint pairs and further confirmed it by experimental PPI. We generate new no-interaction pairs with disjoint classes to update the D-SCRIPT dataset. As result, the AUPR has improved by 10% compared to the D-SCRIPT dataset. Besides, we pre-filter the negatives instead of enumerating all the potential PPI for de-novo PPI network prediction. For E.coli, we can pass around a million negative interactions. To combine the structure and sequence information, we generate a graph for each protein. A graph convolution network using Self-Attention Graph Pooling in Siamese architecture is used to learn these graphs for PPI prediction. In this way, we can improve around 20% in AUPR compared to our baseline model D-SCRIPT.