published_or_final_version / abstract / Botany / Master / Master of Philosophy

published_or_final_version / Paediatrics and Adolescent Medicine / Master / Master of Philosophy

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The availability of large scale protein interaction networks for several species has motivated many comparative studies in recent years. These studies typically employ network alignment algorithms for the task and use the sequence similarity of proteins to aid the alignment process. In this thesis I use a quantitative measure of protein functional similarity and show that the results are superior to sequence based network alignment. I present a method for module detection that combines results from network alignments with clustering measures to achieve superior results over several existing methods. Next, I address the issue of generally low conservation detected by alignments of interaction networks from model organisms. By explicitly modelling evolutionary mechanisms on pairs of networks I test the hypothesis that divergent evolution alone may be the cause. I use a distance metric based on graph summary statistics to assess the fit between experimental and simulated network alignments. Our results indicate that network evolution alone is unlikely to account for the poor quality alignments given by real data. We also find that false positives appear to affect network alignments little compared to false negatives indicating that incompleteness, not spurious links, is the major challenge for interactome-level comparisons. Finally, I focus on the comparative analysis of a subset of the interaction network related to mitosis in Yeast, Human and Fly. Manual ordering of mitosis-related functional annotations allows the study of temporal aspects of the network. I also use a Markov random field approach to infer temporal labels for unlabelled proteins. Sequence based network alignment of the mitotic networks in the three species finds little conservation despite the proteins being functionally very similar. Further investigation suggests a fuzzy relationship between protein sequence and function that may have implications for future network alignment studies.

The thesis work contains two projects under the same umbrella. The first project is to provide a detailed analysis on the behavior of interfacial water molecules at protein-protein complexes, in this case focusing on homodimeric complexes, and to investigate their effect with respect to different residue types. For that reason the homodimeric data-set, which includes high-resolution (≤ 2.30 Å) X-ray crystal structures of 252 (140 Biological & 112 Non-biological) protein complexes was chosen to explore fundamental differences between interfaces that Nature has “engineered” vs. compared to interfaces found under man-made conditions. The data set was comprised of 5391 water molecules where a maximum of 4 Å from both interfacing proteins. Our analysis is applied a suite of modeling tools based on HINT, a program for hydropathic analysis developed in our laboratory. HINT is based on the experimental measurement of the hydrophobic effect. The second project is designed to explore various means of suppressing the expression of human genes that play critical role in HIV pathogenesis. To achieve this aim, a data set of Affymetrix Human HG Focus Target Array, which measures the expression levels of HIV seronegative and seropositive individuals in human PBMCs, was analyzed with Pathway Studio 9.0 software. This work gives insight into the elucidation of the important mechanisms of human proteins interactions in HIV seropositive individuals and their implications. Hence, we found the kind and types of microRNAs that are suppressing the human genes which have great role for HIV replication in a cell.

In this thesis we propose some new approaches to the study of complex networks, and apply them to multiple domains, focusing in particular on protein-protein interaction networks. We begin by examining the roles of individual proteins; specifically, the influential idea of 'date' and 'party' hubs. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. We show that the observations underlying this proposal appear to have been largely illusory, and that topological properties of hubs do not in general correlate with interactor co-expression, thus undermining the primary basis for the categorisation. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins, indicating that it might be useful to conceive of roles for protein-protein interactions, as opposed to individual proteins. The observation that examining just one or a few network properties can be misleading motivates us to attempt to develop a more holistic methodology for network investigation. A wide variety of diagnostics of network structure exist, but studies typically employ only small, largely arbitrarily selected subsets of these. Here we simultaneously investigate many networks using many diagnostics in a data-driven fashion, and demonstrate how this approach serves to organise both networks and diagnostics, as well as to relate network structure to functionally relevant characteristics in a variety of settings. These include finding fast estimators for the solution of hard graph problems, discovering evolutionarily significant aspects of metabolic networks, detecting structural constraints on particular network types, and constructing summary statistics for efficient model-fitting to networks. We use the last mentioned to suggest that duplication-divergence is a feasible mechanism for protein-protein interaction evolution, and that interactions may rewire faster in yeast than in larger genomes like human and fruit fly. Our results help to illuminate protein-protein interaction networks in multiple ways, as well as providing some insight into structure-function relationships in other types of networks. We believe the methodology outlined here can serve as a general-purpose, data-driven approach to aid in the understanding of networked systems.

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science. Johannesburg, 2013. / The forkhead box (FOX) proteins are a family of transcription factors that interact with DNA via a winged helix motif that forms part of the forkhead domain. The FOXP (FOXP1-4) subfamily is unique in the family in that the forkhead domains of these proteins exhibit domain swapping where structural elements are exchanged via extension of the hinge-loop region. The FOXP subfamily members have high sequence homology, yet wild-type FOXP3 is a stable domain-swapped dimer in solution whereas FOXP1 and FOXP2 exist in a monomer/domain-swapped dimer equilibrium. A single amino acid difference is observed in the hinge region of the FOXP subfamily. This corresponds to Tyr540 in FOXP2 and Phe373 in FOXP3. We propose that it is the phenylalanine residue in FOXP3 that shifts the equilibrium towards dimer. Here we use FOXP2 to investigate the effect of a mutation, Y540F, on the structure and dimerisation propensity of the FOXP subfamily. Crystals for the Y540F variant in the presence of DNA have been obtained to demonstrate conclusively that domain swapping occurs. Size-exclusion chromatography indicates that the wild type FOXP2 forkhead domain is almost entirely monomeric at concentrations less than 100 μM. The Y540F variant is shown to stabilise the dimer and the ratio between monomer and dimer is concentration-dependent. DNA binding assays suggest that the Y540F variant binds less favourably to the cognate binding sequence than does the WT FOXP2 forkhead domain. Taken together, these findings suggest that domain swapping may modulate DNA binding.

The G-protein coupled receptors (GPCRs) are seven-transmembrane receptors that transduce signals from the cell surface to intracellular effectors. There are more than 1000 GPCRs in metazoans, while no GPCR has been definitively identified in plants. The most promising plant GPCR candidate, Arabidopsis G-protein coupled receptor 1 (GCR1), physically couples to the G-protein < subunit GPA1 and is involved in cell cycle regulation, blue light and phytohormone responses, but its signalling network remains largely unknown. This project aimed to achieve a better understanding of GCR1 signalling by identifying its interactors using a novel yeast two hybrid system – the Ras Recruitment System (RRS). Screening of an Arabidopsis cDNA library using a bait comprising intracellular loop 1 (i1) and 2 (i2) of GCR1 resulted in the isolation of 20 potential interactors. Extensive reconfirmation screening demonstrated that three of these interactors: Thioredoxin h3 (TRX3), Thioredoxin h4 (TRX4) and a DHHC type zinc finger family protein (zf-DHHC1) interact specifically with both i1 and i2 of GCR1. This was supported by the reverse RRS (rRRS) and 6xHis-pull-down assays. It is speculated that TRX3 and TRX4, which can reduce disulfide bridges of target proteins and act as powerful antioxidants, may regulate GCR1-mediated signalling events in response to oxidative stress. Alternatively, they may modulate GCR1 targeting or signalling through their chaperone activities. zf-DHHC1 has a predicted membrane topography that is shared by most DHHC domain-containing palmitoyl acyl transferases. It may modify GCR1 activity through palmitoylation of the two cysteines located at the cytoplasmic end of the first transmembrane domain. Together, these findings contribute to the growing understanding of the GCR1 signalling network, and provide valuable starting points for further investigation.