Characterization of SCF Ubiquitin-Ligase Subunits in Arabidopsis

The Arabidopsis thaliana genome encodes several families of polypeptides that are known or predicted to participate in the formation of the SCF-class of E3-ubiquitin ligase complexes. One such gene family encodes the Skp1-like class of polypeptide subunits, where 21 genes have been identified and are known to be expressed in Arabidopsis. The complexity of this family of Arabidopsis Skp1-like--or ASK --genes, together with the close structural similarity among its members, raises the prospect of significant functional redundancy among select paralogs. We have assessed the potential for functional redundancy within the ASK gene family by analyzing an expanded set of criteria that define redundancy with higher resolution. The criteria used include quantitative expression of locus-specific transcripts using qRT-PCR, assessment of the sub-cellular localization of individual ASK:YFP auto-fluorescent fusion proteins expressed in vivo, as well as the in planta assessment of individual ASK-F-box protein interactions using BiFC. The results indicated significant functional divergence of steadystate transcript abundance and protein-protein interaction specificity involving ASK proteins in a pattern that is poorly predicted by sequence-based phylogeny. The information emerging from this and related studies was used to functionally characterize using an RNAi approach complemented by phenotypical analysis. The observation of diverse phenotypes not only argues a high level of sub-functionalization has occurred throughout the ASK gene family, but also underscores the breadth of functions that this gene family plays throughout plant development. Transport Inhibitor Response (TIR1), is a member of a family of five Auxin-signaling F-box proteins (AFBs) and has been shown to act as the receptor for auxin binding and activation of the SCF TIR1 complex, leading to targeted protein degradation events involved in auxin perception. We provide evidence for homo-dimerization of TIR1 protein in planta together with a role for TIR1 homo-dimerization in the degradation of Aux/IAA proteins as part of the auxin-signaling pathway

Prediction of protein-protein interaction types using machine learning approaches

Prediction and analysis of protein-protein interactions (PPIs) is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. One of the important problems surrounding PPIs is the identification and prediction of different types of complexes, which are characterized by properties such as type and numbers of proteins that interact, stability of the proteins, and also duration of the interactions. This thesis focuses on studying the temporal and stability aspects of the PPIs mostly using structural data. We have addressed the problem of predicting obligate and non-obligate protein complexes, as well as those aspects related to transient versus permanent because of the importance of non-obligate and transient complexes as therapeutic targets for drug discovery and development. We have presented a computational model to predict-protein interaction types using our proposed physicochemical features of desolvation and electrostatic energies and also structural and sequence domain-based features. To achieve a comprehensive comparison and demonstrate the strength of our proposed features to predict PPI types, we have also computed a wide range of previously used properties for prediction including physical features of interface area, chemical features of hydrophobicity and amino acid composition, physicochemical features of solvent-accessible surface area (SASA) and atomic contact vectors (ACV). After extracting the main features of the complexes, a variety of machine learning approaches have been used to predict PPI types. The prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative and relevant properties to distinguish between these two types of complexes Our computational results on different datasets confirm that using our proposed physicochemical features of desolvation and electrostatic energies lead to significant improvements on prediction performance. Moreover, using structural and sequence domains of CATH and Pfam and doing biological analysis help us to achieve a better insight on obligate and non-obligate complexes and their interactions

Covalent modification and intrinsic disorder in the stability of the proneural protein Neurogenin 2

Neurogenin 2 (Ngn2) is a basic Helix-Loop-Helix (bHLH) transcription factor regulating differentiation and cell cycle exit in the developing brain. By transcriptional upregulation of a cascade of other bHLH factors, neural progenitor cells exit the cell cycle and differentiate towards a neuronal fate. Xenopus laevis Ngn2 (xNgn2) is a short-lived protein, targeted for degradation by the 26S proteasome. I have investigated the stability of Ngn2 mediated by post-translational modifications and structural disorder. Firstly I will describe work focused on ubiquitylation of xNgn2, targeting it for proteasomal degradation. xNgn2 is ubiquitylated on lysines, the recognized site of modification. I will discuss the role of lysines in ubiquitylation and stability of xNgn2. In addition to canonical ubiquitylation on lysines, I describe ubiquitylation of xNgn2 on non-canonical sites, namely its amino-terminal amino group, and cysteine, serine and threonine residues. I show that the ubiquitylation of cysteines in particular exhibits cell cycle dependence and is also observed in mammalian cell lines, resulting in cell cycle-dependent regulation of stability. I will then discuss whether phosphorylation, a regulator of xNgn2 activity, also affects xNgn2 stability. I will provide evidence of cell cycle-dependent phosphorylation of cyclin dependent kinase (cdk) consensus sites affecting the stability of xNgn2. Finally I describe studies on the folding properties of Ngn2 to assess their role in protein stability. xNgn2 associates with DNA and its heterodimeric binding partner xE12 and may interact directly with the cyclin-dependent kinase inhibitor Xic1. I will discuss the role of these interaction partners in xNgn2 stability. xNeuroD, a downstream target of xNgn2, is a related bHLH transcription factor which is stable. Here I describe domain swapping experiments between these two proteins highlighting regions conferring instability on the chimeric protein. Finally I will provide nuclear magnetic resonance (NMR) data looking at the effect of phosphorylation on protein structure in mouse Ngn2 (mNgn2).This work was supported by a Medical Research Council Studentship

Investigation of proteins and their modifications using high-resolution mass spectrometry

Advances in mass spectrometry (MS) has allowed for the deep analysis of various proteomes, providing identifications of proteins and their modifications. The true power in modern-day proteomics is the application of MS techniques to address various biological questions, propelling disease research and biochemical understanding of organisms. We have utilized high-resolution mass spectrometry to investigate biological questions leading to a greater knowledge of cellular biology. The transcriptional co-activator with PDZ-binding motif (TAZ), is regulated by reversible phosphorylation. However, sequence analysis suggests many potential uncharacterized sites of TAZ phosphorylation, specifically in regions in close proximity to a critical phosphorylation site making site assignment challenging. Using both targeted and untargeted approaches, we identified novel TAZ phosphorylation sites, using a reaction monitoring scheme to resolve positional phosphoisomers, and determined the biological consequence of a novel site, serine 93, on TAZ localization. Spinal muscular atrophy (SMA) is a motor neuron disease affecting 1 in 10,000 individuals. SMA has been shown to involve the release of extracellular vesicles (EVs), which have been used as a source of biomarkers for disease. We examined the use of EVs as a source of SMA biomarkers. We isolated and quantified \u3e650 proteins from SMA-derived vesicles finding potential biomarkers, one of which was confirmed in patients, suggesting these vesicles coupled with our methods are suitable for SMA biomarker discovery. In the model plant species Arabidopsis thaliana gene expression is heavily regulated through post-translational ubiquitination, however a gap between the number of ubiquitinated substrates identified and genes encoding the ubiquitin machinery exists, suggesting many unidentified modifications exist. The main strategy for studying ubiquitomes across species uses diglycine enrichment followed by MS analysis. We developed a DIA-based MS method coupled with novel sample preparation methods to overcome plant-specific challenges and increase the repository of the Arabidopsis ubiquitome, identifying 160 proteins with over 400 ubiquitination sites. The prevalent Charcot-Marie-Tooth disease can be caused by mutations in the lipid phosphatase MTMR2, a protein critical for regulating endosomal dynamics. MTMR2 is regulated by phosphorylation at serine 58. However, the phosphatase and the alterations in protein-protein interactions occurring with this modification have not been thoroughly investigated. To isolate MTMR2 interacting proteins, we utilized in vivo labeling fusing BirA biotin ligase to MTMR2, followed by MS analysis, identifying a putative interactor, TSSC1. We also provide evidence that MTMR2 itself may be subjected to phosphorylation-dependent degradation. This work utilizes high-resolution MS techniques to link protein regulation and function in a variety of biological and cellular contexts. The techniques presented here can be applied to address the gaps of knowledge in various proteomes and are amenable to user-specific modifications. The techniques here provide a framework for determining disease biomarkers for neurological diseases from EVs, investigating proteome-wide changes through protein modifications, and ultimately link high-resolution analytical mass spectrometry techniques and data to address critical biological events in a robust fashion

Inferring structures, free energy differences, and kinetic rates of biological macromolecular assemblies by integrative modeling

Biological macromolecular assemblies play crucial roles in most cellular processes. The determination of their structures, thermodynamics, and kinetics is essential to understand their function, evolution, modulation, and design. Determining such models, however, remains challenging. One particularly powerful approach to constructing models in general is integrative modeling. Integrative modeling aims to maximize the accuracy, precision, and completeness of models, by simultaneously utilizing all available information, including experimental data, physical principles, statistical analyses, and other prior models. The goal of this thesis is to expand the scope of integrative modeling to the inference of spatial, thermodynamic, and kinetic aspects of macromolecular assemblies. In Chapter I, I introduce the integrative modeling framework for spatiotemporal modeling of biological macromolecular assemblies. In Chapter II, I demonstrate how the synergy between multi-chemistry cross-linking mass spectrometry and integrative modeling can map the structural dynamics of macromolecular assemblies, by application to the human Cop9 signalosome complex. In Chapter III, I present a method for determining structures, free energy differences, and kinetic rates of macromolecular assemblies along their functional cycle, mainly from negative stain electron microscopy (EM). We apply the method to the yeast Hsp90 to estimate the free energy differences and kinetic parameters along its nucleotide hydrolysis cycle, which includes open and closed states of Hsp90. In Chapter IV, I describe a validation of stochastic sampling in integrative modeling. The remaining chapters describe applications of integrative modeling to assemblies of various sizes and scales, using various sources of information, thus illustrating the flexibility of the integrative modeling approach. Specifically, I apply integrative modeling to the human ECM29-Proteasome assembly under oxidative stress (Chapter V), the yeast nuclear pore complex (NPC) cytoplasmic mRNA export platform (Chapter VI), the major membrane ring component of the yeast NPC (Chapter VII), the entire yeast NPC (Chapter VIII), and the reconstruction of 3D structures of MET antibodies (Chapter IX)

Computational approaches for identifying inhibitors of protein interactions

Inter-molecular interaction is at the heart of biological function. Proteins can interact with ligands, peptides, small molecules, and other proteins to serve their structural or functional purpose. With advances in combinatorial chemistry and the development of high throughput binding assays, the available inter-molecular interaction data is increasing exponentially. As the space of testable compounds increases, the complexity and cost of finding a suitable inhibitor for a protein interaction increases. Computational drug discovery plays an important role in minimizing the time and cost needed to study the space of testable compounds. This work focuses on the usage of various computational methods in identifying protein interaction inhibitors and demonstrates the ability of computational drug discovery to contribute to the ever growing field of molecular interaction. A program to predict the location of binding surfaces on proteins, STP (Mehio et al., Bioinformatics, 2010, in press), has been created based on calculating the propensity of triplet-patterns of surface protein atoms that occur in binding sites. The use of STP in predicting ligand binding sites, allosteric binding sites, enzyme classification numbers, and binding details in multi-unit complexes is demonstrated. STP has been integrated into the in-house high throughput drug discovery pipeline, allowing the identification of inhibitors for proteins whose binding sites are unknown. Another computational paradigm is introduced, creating a virtual library of -turn peptidomimetics, designed to mimic the interaction of the Baff-Receptor (Baff-R) with the B-Lymphocyte Stimulator (Blys). LIDAEUS (Taylor, et al., Br J Pharmacol, 2008; 153, p. S55-S67) is used to identify chemical groups with favorable binding to Blys. Natural and non-natural sidechains are then used to create a library of synthesizable cyclic hexapeptides that would mimic the Blys:Baff-R interaction. Finally, this work demonstrates the usage and synergy of various in-house computational resources in drug discovery. The ProPep database is a repository used to study trends, motifs, residue pairing frequencies, and aminoacid enrichment propensities in protein-peptide interaction. The LHRLL protein-peptide interaction motif is identified and used with UFSRAT (S. Shave, PhD Thesis, University of Edinburgh, 2010) to conduct ligand-based virtual screening and generate a list of possible antagonists from the EDULISS (K. Hsin, PhD Thesis, University of Edinburgh, 2010) compound repository. A high throughput version of AutoDock (Morris, et al., J Comput Chem, 1998; 19, p. 1639-62) was adapted and used for precision virtual screening of these molecules, resulting in a list of compounds that are likely to inhibit the binding of this motif to several Nuclear Receptors

Characterizing Signal Transduction Networks and Biological Responses Using Computer Simulations and Machine Learning

The use of computer simulations in biology is often limited due to the lack of experimentally measured parameters. In these scenarios, parameter exploration can be used to probe biological systems and refine understanding of biological mechanisms. For systems with few unknown parameters, parameter sweeps that concurrently vary all unknown parameters are tractable. In complex systems with many unknown parameters, supervised machine learning algorithms can be used to discover parameters leading to targeted system responses. In this thesis, we study three biological problems in which we use parameter exploration methods to gain mechanistic insights. We first explore the role of altered metabolism in cancer cells that reside in heterogeneous tumor microenvironments. We use a multiscale, hybrid cellular automaton model to evaluate tumor progression while varying malignant cell traits using a systematic parameter sweep. The results reveal distinct growth regimes associated with varied malignant cell traits. We then study kinetic mechanisms governing fixed-topology signal transduction networks and use evolutionary algorithms to discover kinetic parameters that produce specified network responses. We analyze the growth-response network in Arabidopsis with this supervised machine learning approach. This allows us to identify constraints on kinetic parameters that govern the observed responses. The evolved parameters are used to calculate the responses of individual network components, which are used to generate hypotheses that can be tested in vivo to help determine the network topology. We finally apply a similar approach to redesign signal transduction networks. We demonstrate that the T cell receptor network and an oscillator network show remarkable flexibility in generating altered responses to input, and we further use a nonlinear clustering method to identify design criteria for the underlying kinetic parameters. For each project, observations produced from in silico simulations lead to the formation of hypotheses that are experimentally testable