Maintaining genomic integrity is important for any organism. DNA mismatch repair (MMR) serves to correct errors that occur during DNA replication and recombination, such as unpaired bases or mismatched bases. Mutl is a key player and serves to coordinate protein-protein interactions. Recently it has been shown that human Mutl functions as an endonuclease and that this activity is imperative for functioning MMR. In this work, the X-ray crystal structure of the C-terminal endonuclease domain of Bacillus subtilis Mutl (BsMutL-CTD) is presented. Diffraction quality crystals of BsMutL-CTD were grown using vapor diffusion. The crystal structure of BsMutL-CTD was solved using multiwavelength anomalous diffraction. The structure reveals a putative metal binding site which clusters closely in space with endonuclease motif. Using the structure and sequence homology, several mutations were made and an investigation into the endonuclease activity of BsMutL was performed. BsMutL was confirmed to be a manganese-dependent endonuclease and key residues which contribute to endonuclease function were identified. / Thesis / Master of Science (MSc)

Adherens junctions are a main cell-cell adhesion structure found in epithelial cells. The stability of adherens junctions is attributed to various protein-signaling cascades and importantly the interaction between the transmembrane protein E-cadherin and cytoplasmic p120 catenin. This interaction is critical for cell adhesion and prevention of uncontrolled growth in normal cells. The interaction interface between these two binding partners was previously determined to comprise p120's Armadillo repeat domain (p120Arm) and Ecadherin's cytoplasmic juxtamembrane domain (Ecadc). Based on this information, peptide aptamers were derived from p120Arm and their interaction with Ecadc was tested in vitro. We reasoned that those could be expressed in vivo to stabilize adherens junctions at the cell-cell junction. In this study, we established protein-protein interaction assays to demonstrate p120Arm's ability to bind Ecadc and then used these assays to determine if p120Arm-derived peptides may competitively bind Ecadc. We demonstrated the interaction between p120Arm and Ecadc using assays that were not previously used such as: co-precipitation, analytical gel filtration and the bacterial-2-hybrid assay. However, the p120Arm-derived peptides did not bind to Ecadc or compete its interaction with p120Arm. This may be due to the nature of the assays that may not reflect competitive binding or the aptamers may not adopt the native conformation preventing binding to Ecadc. / Thesis / Master of Science (MSc)

β-Glucosidase zymograms of certain maize genotypes (nulls) do not show any activity bands after electrophoresis. We have shown that a chimeric lectin called β-glucosidase aggregating factor (BGAF) is responsible for the absence of β-glucosidase activity bands on zymograms. BGAF specifically binds to maize β-glucosidase isozymes Glu1 and Glu2 and forms large, insoluble complexes. Furthermore, we have previously shown that the N-terminal (Glu⁵⁰-Val¹⁴⁵) and the C-terminal (Phe⁴⁶⁶-Ala⁵¹²) regions contain residues that make up the BGAF binding site on maize Glu1. However, sequence comparison between sorghum β-glucosidases (dhurrinases, Dhr1 and Dhr2), to which BGAF does not bind, and maize β-glucosidases, and an examination of the 3-D structure of Glu1 suggested that the BGAF binding site on Glu1 is much smaller than predicted previously. To define more precisely the BGAF binding site, we constructed additional chimeric β-glucosidases. The results showed that a region spanning 11 amino acids (Ile⁷²-Thr⁸²) on Glu1 is essential and sufficient for BGAF binding, whereas the extreme N-terminal region Ser¹-Thr²⁹, together with C-terminal region Phe⁴⁶⁶-Ala⁵¹², affects the size of Glu1-BGAF complexes. To determine the importance of each region for binding, we determined the dissociation constants (K_d) of chimeric β-glucosidase-BGAF interactions. The results demonstrated that the extreme N-terminal and C-terminal regions are important but not essential for binding. To confirm the importance of Ile⁷²-Thr⁸² on Glu1 for BGAF binding, we constructed chimeric Dhr2 (C-11, Dhr2 whose Val⁷²-Glu⁸² region was replaced with the Ile⁷²-Thr⁸² region of Glu1). C-11 binds to BGAF, indicating that the Ile⁷²-Thr⁸² region is indeed a major interaction site on Glu1 involved in BGAF binding. We also constructed mutant β-glucosidases to identify and define the contribution of individual amino acids in the above three regions to BGAF binding. In the N-terminal region (Ile⁷²-Thr⁸²), critical region for BGAF binding, Glu1 mutants K81E and T82Y failed to bind BGAF in the gel-shift assay and their frontal affinity chromatography (FAC) profiles were essentially similar to that of sorghum β-glucosidase (dhurrinase 2, Dhr2), a non-binder, indicating that these two amino acids within Ile⁷²-Thr⁸² region are essential for BGAF binding. In the extreme N-terminal (Ser¹-Thr²⁹) and C-terminal (Phe⁴⁶⁶-Ala⁵¹²) regions, N481E [substitution of asparagine-481 with glutamic acid (as in Dhr)] showed lower affinity for BGAF, whereas none of the single amino acid substitutions in the Ser¹-Thr²⁹ region showed any effect on BGAF binding indicating that these regions play a minor role. To further confirm the importance of lysine-81 and threonine-82 for BGAF binding, we produced a number of Dhr2 mutants, and the results showed that all four unique amino acids (isoleucine-72, asparagine-75, lysine-81, and threonine-82) of Glu1 in the peptide span Ile⁷²-Thr⁸² are required to impart BGAF binding ability to Dhr2. The sequence comparison among plant β-glucosidases supports the hypothesis that BGAF binding is specific to maize β-glucosidases because only maize β-glucosidases have threonine at position 82. / Ph. D.

Our goal is to investigate protein networks conserved between different organisms. Given the protein interaction networks for two species and a list of homologous pairs of protein in the two species, we propose a model for measuring whether two subnetworks, one in each protein interaction network, are conserved. Our model separately measures the degree of conservation of the two subnetworks and the quality of the edges in each subnetwork. We propose an algorithm for finding pairs of networks, one in each protein interaction network, with high conservation and high quality. When applied to publicly-available protein-protein interaction data and gene sequences for baker's yeast and fruit fly, our algorithm finds many conserved networks with a high degree of functional enrichment. Using our method, we find many conserved protein interaction networks involved in functions such as DNA replication, protein folding, response to heat, protein serine/threonine phosphatase activity, kinase activity, and ATPase activity. / Master of Science

Yes / The Cdc42- and Rac-interactive binding motif (CRIB) of coronin binds to Rho GTPases with a preference for GDP-loaded Rac. Mutation of the Cdc42- and Rac-interactive binding motif abrogates Rac binding. This results in increased 1evels of activated Rac in coronin-deficient Dictyostelium cells (corA−), which impacts myosin II assembly. corA− cells show increased accumulation of myosin II in the cortex of growth-phase cells. Myosin II assembly is regulated by myosin heavy chain kinase–mediated phosphorylation of its tail. Kinase activity depends on the activation state of the p21-activated kinase a. The myosin II defect of corA− mutant is alleviated by dominant-negative p21-activated kinase a. It is rescued by wild-type coronin, whereas coronin carrying a mutated Cdc42- and Rac-interactive binding motif failed to rescue the myosin defect in corA− mutant cells. Ectopically expressed myosin heavy chain kinases affinity purified from corA− cells show reduced kinase activity. We propose that coronin through its affinity for GDP–Rac regulates the availability of GTP–Rac for activation of downstream effectors. / This work was supported by Deutsche Forschungsgemeinschaft (DFG), Sonderforschungsbereich 670 (SFB 670) and Köln Fortune (to A.A.N.). A.M.-T. acknowledges support by the SFB 914, and J.F. acknowledges support by Grant FA330/6-1 within the framework of the DFG priority programme “Principles and Evolution of Actin Nucleator Complexes” (SPP1464). Work in F.R. lab is supported by grants from the Hull York Medical School.

The world has proven unprepared for deadly viral outbreaks. Designing antiviral drugs and strategies requires a firm understanding of the interactions taken place between the proteins of the virus and human proteins. The current computational models for predicting these interactions consider only single viruses for which extensive prior knowledge is available. The two prediction frameworks in this dissertation, DeNovo and DeNovo-Human, make it possible for the first time to predict the interactions between any viral protein and human proteins. They further helped to answer critical questions about the Zika virus. DeNovo utilizes concepts from virology, bioinformatics, and machine learning to make predictions for novel viruses possible. It pools protein-protein interactions (PPIs) from different viruses sharing the same host. It further introduces taxonomic partitioning to make the reported performance reflect the situation of predicting for a novel virus. DeNovo avoids the expected low accuracy of such a prediction by introducing a negative sampling scheme that is based on sequence similarity. DeNovo achieved accuracy up to 81% and 86% when predicting for a new viral species and a new viral family, respectively. This result is comparable to the best achieved previously in single virus-host and intra-species PPI prediction cases. DeNovo predicts PPIs of a novel virus without requiring known PPIs for it, but with a limitation on the number of human proteins it can make predictions against. The second framework, DeNovo-Human, relaxes this limitation by forcing in-network prediction and random sampling while keeping the pooling technique of DeNovo. The accuracy and AUC are both promising ($>85%$, and $>91%$ respectively). DeNovo-Human facilitates predicting the virus-human PPI network. To demonstrate how the two frameworks can enrich our knowledge about virus behavior, I use them to answer interesting questions about the Zika virus. The research questions examine how the Zika virus enters human cells, fights the innate immune system, and causes microcephaly. The answers obtained are well supported by recently published Zika virus studies. / Ph. D. / When a virus attacks a human body, it disturbs the host cells by interacting with their proteins. Identifying these interactions is key to fighting the virus. In this dissertation, I developed two computational tools to identify the interactions for any virus infecting the human. I further used these tools to answer interesting questions about the Zika virus behavior. The results are in agreement with recently published experimental studies about the virus.

Molecular Biology has allowed the characterization and manipulation of the molecules of life in the wet lab. Also the structures of those macromolecules are being continuously elucidated. During the last decades of the past century, there was an increasing interest to study how the different genes are organized into different organisms (‘genomes’) and how those genes are expressed into proteins to achieve their functions. Currently the sequences for many genes over several genomes have been determined. In parallel, the efforts to have the structure of the proteins coded by those genes go on. However it is experimentally much harder to obtain the structure of a protein, rather than just its sequence. For this reason, the number of protein structures available in databases is an order of magnitude or so lower than protein sequences. Furthermore, in order to understand how living organisms work at molecular level we need the information about the interaction of those proteins. Elucidating the structure of protein macromolecular assemblies is still more difficult. To that end, the use of computers to predict the structure of these complexes has gained interest over the last decades. The main subject of this thesis is the evaluation of current available computational methods to predict protein – protein interactions and build an atomic model of the complex. The core of the thesis is the evaluation protocol I have developed at Service de Conformation des Macromolécules Biologiques et de Bioinformatique, Université Libre de Bruxelles, and its computer implementation. This method has been massively used to evaluate the results on blind protein – protein interaction prediction in the context of the world-wide experiment CAPRI, which have been thoroughly reviewed in several publications [1-3]. In this experiment the structure of a protein complex (‘the target’) had to be modeled starting from the coordinates of the isolated molecules, prior to the release of the structure of the complex (this is commonly referred as ‘docking’). The assessment protocol let us compute some parameters to rank docking models according to their quality, into 3 main categories: ‘Highly Accurate’, ‘Medium Accurate’, ‘Acceptable’ and ‘Incorrect’. The efficiency of our evaluation and ranking is clearly shown, even for borderline cases between categories. The correlation of the ranking parameters is analyzed further. In the same section where the evaluation protocol is presented, the ranking participants give to their predictions is also studied, since often, good solutions are not easily recognized among the pool of computer generated decoys. An overview of the CAPRI results made per target structure and per participant regarding the computational method they used and the difficulty of the complex. Also in CAPRI there is a new ongoing experiment about scoring previously and anonymously generated models by other participants (the ‘Scoring’ experiment). Its promising results are also analyzed, in respect of the original CAPRI experiment. The Scoring experiment was a step towards the use of combine methods to predict the structure of protein – protein complexes. We discuss here its possible application to predict the structure of protein complexes, from a clustering study on the different results. In the last chapter of the thesis, I present the preliminary results of an ongoing study on the conformational changes in protein structures upon complexation, as those rearrangements pose serious limitations to current computational methods predicting the structure protein complexes. Protein structures are classified according to the magnitude of its conformational re-arrangement and the involvement of interfaces and particular secondary structure elements is discussed. At the end of the chapter, some guidelines and future work is proposed to complete the survey.

Oxidative stress is an important factor in carcinogenesis of oncohematological diseases. However its role in the pathogenesis of myelodysplastic syndromes (MDS) remains unclear. In this study, we have determined the oxidative status and evaluated proteomic changes in plasma of MDS patients as a consequence of oxidative dysbalance (oxidative modifications, protein-protein interaction and complex forming). We measured the levels of total cysteine, homocysteine, cysteinyglycine, glutathione, nitrites and nitrates in the plasma from 61 MDS patients and 23 healthy donors using high performance liquid chromatography. Glutathione and nitrites levels reduced significantly while other aminothiols levels increased significantly in plasma of MDS patients. This association with oxidative stress did not correlate with iron overload. We also found enhanced levels of asymmetric dimethylarginine in serums of middle aged patients with MDS that correlate to posttranslational modifications of proteins arginyl residues. Furthermore, carbonylated proteins level was significantly elevated in MDS patients compared to healthy donors. Using mass spectrometry, 5 S-nitrosylated blood platelets proteins were identified in plasma and blood platelets of MDS patients and set of 16 plasma proteins with high probability of...

Le fonctionnement de tout organisme vivant repose sur l'activité, ou fonction, des protéines présentes dans les cellules. Sachant que les protéines travaillent en équipe, donc interagissent physiquement pour assurer leurs fonctions, il est naturel d'étudier la fonction des protéines à travers leurs interactions.Grâce au développement de techniques d'identification d'interactions entre protéines, des cartes d'interactions ont été construites, et leur analyse avec des méthodes de classification permet une meilleur compréhension de la fonction des protéines au sein des cellules. Cependant, le développement de nouvelles méthodes de classification et leur évaluation est indispensable pour répondre aux nouvelles questions biologiques posées. Ce travail est basé sur le développement de deux méthodes de classification, qui sont évaluées au niveau méthodologique et biologique, les méthodes d'évaluation elles-mêmes étant finalement questionnées. / All living organisms depend of the activity, or function, of proteins present in the cells. Knowing that proteins work as a team, so physically interact to perform their functions, it is natural to study protein function through their interactions.With the development of techniques to identify protein interactions, interactions maps were built, and their analysis with clustering methods led to a better understanding of protein functions within cells. However, the development of new clustering methods and their evaluation is essential to answer to new biological questions.This work is based on the development of two clustering methods, which are methodologically and biologically evaluated, the evaluation methods themselves being finally questioned.

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