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La réplication et l’assemblage du virus de l’hépatite C (VHC) sont régulés finement dans le temps et l’espace par les interactions protéiques entre le virus avec l’hôte. La compréhension de la biologie du virus ainsi que sa pathogénicité passe par les connaissances relatives aux interactions virus/hôte. Afin d’identifier ces interactions, nous avons exploité une approche d’immunoprécipitation (IP) couplée à une détection par spectrométrie de masse (MS), pour ensuite évaluer le rôle des protéines identifiées dans le cycle viral par une technique de silençage génique. Les protéines virales Core, NS2, NS3/4A, NS4B, NS5A et NS5B ont été exprimées individuellement dans les cellules humaines 293T et immunoprécipitées afin d’isoler des complexes protéiques qui ont été soumis à l’analyse MS. Ainsi, 98 protéines de l’hôte ont été identifiées avec un enrichissement significatif et illustrant une spécificité d’interaction. L’enrichissement de protéines connues dans la littérature a démontré la force de l’approche, ainsi que la validation de 6 nouvelles interactions virus/hôte. Enfin, le rôle de ces interactants sur la réplication virale a été évalué dans un criblage génomique par ARN interférant (ARNi). Deux systèmes rapporteurs de la réplication virale ont été utilisés : le système de réplicon sous-génomique (Huh7-Con1-Fluc) et le système infectieux (J6/JFH-1/p7Rluc2a), ainsi qu’un essai de toxicité cellulaire (Alamar Blue). Parmi les protéines de l’hôte interagissant avec le VHC, 28 protéines ont démontré un effet significatif sans effet de toxicité cellulaire, suggérant fortement un rôle dans la réplication du VHC. Globalement, l’étude a mené à l’identification de nouvelles interactions virus/hôte et l’identification de nouvelles cibles thérapeutiques potentielles. / Hepatitis C virus (HCV) replication and assembly are tightly regulated in time and space within the cell, most likely due to protein interactions between virus and host. In order to better understand HCV biology and its pathogenesis, there is a need to unravel virus/host interaction network. We extended our knowledge of virus/host interactions by the identification of cellular proteins associated to HCV proteins using an immunoprecipitation (IP) technique coupled to mass spectrometry (MS), and further evaluate the role of retrieved interactors using gene knockdown. FLAG-tagged viral proteins Core, NS2, NS3/4A, NS4B, NS5A and NS5B have been expressed individually in 293T human cells, and immunoprecipitated protein complexes have been submitted to MS analysis for identification of host proteins. In this study, 98 proteins were significantly enriched and showed specific interaction to a viral protein. Retrieval of previously characterized interacting proteins proved the strength of the method. Six newly identified interactors by MS were individually confirmed using IP of viral proteins. We evaluated the role of identified interactors in HCV replication by performing a functional lentivirus-based RNA interference (RNAi) screen. Two reporter systems were used: the sub- genomic replicon (Huh7-Con1-Fluc) and a full length infectious clone (J6/JFH-1/p7Rluc2a), as well as the cellular toxicity assay Alamar blue. Of the identified host interactors, 28 proteins showed a significant effect on HCV replication upon gene knockdown and without cellular toxicity. Overall, the study led to the identification of novel virus/host interactions essential in HCV life cycle and provides novel potential drug targets.

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Cette thèse porte sur deux sujets intrinsèquement liés : le calcul de la constante de normalisation d’un champ de Markov et l’estimation de l’affinité de liaison d’un complexe de protéines. Premièrement, afin d’aborder ce problème de comptage #P complet, nous avons développé Z*, basé sur un élagage des quantités de potentiels négligeables. Il s’est montré plus performant que des méthodes de l’état de l’art sur des instances issues d’interaction protéine-protéine. Par la suite, nous avons développé #HBFS, un algorithme avec une garantie anytime, qui s’est révélé plus performant que son prédécesseur. Enfin, nous avons développé BTDZ, un algorithme exact basé sur une décomposition arborescente qui a fait ses preuves sur des instances issues d’interaction intermoléculaire appelées “superhélices”. Ces algorithmes s’appuient sur des méthodes issuse des modèles graphiques : cohérences locales, élimination de variable et décompositions arborescentes. A l’aide de méthodes d’optimisation existantes, de Z* et des fonctions d’énergie de Rosetta, nous avons développé un logiciel open source estimant la constante d’affinité d’un complexe protéine protéine sur une librairie de mutants. Nous avons analysé nos estimations sur un jeu de données de complexes de protéines et nous les avons confronté à deux approches de l’état de l’art. Il en est ressorti que notre outil était qualitativement meilleur que ces méthodes. / This thesis is focused on two intrinsically related subjects : the computation of the normalizing constant of a Markov random field and the estimation of the binding affinity of protein-protein interactions. First, to tackle this #P-complete counting problem, we developed Z*, based on the pruning of negligible potential quantities. It has been shown to be more efficient than various state-of-the-art methods on instances derived from protein-protein interaction models. Then, we developed #HBFS, an anytime guaranteed counting algorithm which proved to be even better than its predecessor. Finally, we developed BTDZ, an exact algorithm based on tree decomposition. BTDZ has already proven its efficiency on intances from coiled coil protein interactions. These algorithms all rely on methods stemming from graphical models : local consistencies, variable elimination and tree decomposition. With the help of existing optimization algorithms, Z* and Rosetta energy functions, we developed a package that estimates the binding affinity of a set of mutants in a protein-protein interaction. We statistically analyzed our esti- mation on a database of binding affinities and confronted it with state-of-the-art methods. It appears that our software is qualitatively better than these methods.

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Le domaine de la bioénergie moléculaire concerne le transfert et le stockage d’énergie dans les cellules biologiques. Ce projet s’articule autour de la respiration et plus précisément le mécanisme de pompage de sodium et de protons, et son couplage au transfert d’électrons. Premièrement, nous nous sommes intéressés au pompage d’ions sodium par la NADH : quinone oxidoreductase de la bactérie Vibrio cholerae. L’importance de flavines spécifiques et des résidus acides dans le transfert de sodium ont été démontrée. Par la suite, l’interaction entre protéines, notamment le cytochrome c552 et le fragment CuA de l’oxidase de type ba3 de l’organisme Thermus thermophilus a été étudié. Une réorganisation structurelle induit par le transfert d’électron a été démontrée par la spectroscopie IRTF différentielle. Enfin, dans la dernière partie de ce travail, l’interaction au sein du supercomplex bc1-aa3 de la chaîne respiratoire du Corynebacterium glutamicum a été analysée. / The field of molecular bioenergetics deals with the energy transduction in biological cells. In this project, respiration and more specifically proton and sodium pumping enzymes and their coupling to electron transfer have been in focus. First we have been interested in the Na+-pumping NADH:quinone reductase from Vibrio cholerae which is the entry site of electrons in the respiratory chain of several pathogens. The role of specific flavin cofactors and amino acids involved in Na+ transfer has been shown in a combined IR spectroscopic and electrochemical approach. The interaction between proteins, namely the cytochrome c552 and the CuA fragment from the terminal ba3 oxidase from the organism Thermus thermophilus was then investigated. Structural reorganization during electron transfer was revealed by IR spectroscopy. Finally, in the third part of the project the interaction within the bc1-aa3 supercomplex from the respiratory chain from Corynebacterium glutamicum was analyzed.

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Greater understanding of how we can use protein simulations and statistical characteristics of biomolecular interfaces as proxies for biological function will make manifest major advances in protein engineering. Here we show how to use calculated change in binding affinity and coevolutionary scores to predict the functional effect of mutations in the interface between a Histidine Kinase and a Response Regulator. These proteins participate in the Two-Component Regulatory system, a system for intracellular signalling found in bacteria. We find that both scores work as proxies for functional mutants and demonstrate a ~30 fold improvement in initial positive predictive value compared with choosing randomly from a sequence space of 160 000 variants in the top 20 mutants. We also demonstrate qualitative differences in the predictions of the two scores, primarily a tendency for the coevolutionary score to miss out on one class of functional mutants with enriched frequency of the amino acid threonine in one position.

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.<p>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’).<p>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.<p>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.<p>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. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished