Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

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.

No description available.

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.

No description available.

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

L'auxine est une hormone végétale qui coordonne plusieurs processus de développement des plantes à travers la régulation d'un ensemble spécifique de gènes. Les Auxin Response Factors (ARF) sont des régulateurs transcriptionnels qui modulent l'expression de gènes de réponse à l’auxine. Des données récentes montrent que les membres de la famille des ARF sont impliqués dans la régulation du développement des fruits de la nouaison à la maturation. L'objectif principal de la thèse est d’étudier la part qui revient aux ARF dans le contrôle du développement et de la maturation des fruits et d’en comprendre les mécanismes d’action. L’analyse des données d’expression disponibles dans les bases de données a révélé que, parmi tous les ARF de tomates, SlARF2 affiche le plu haut niveau d'expression dans le fruit avec un profil distinctif d’expression associé à la maturation. Nous avons alors entrepris la caractérisation fonctionnelle de SlARF2 afin d’explorer son rôle dans le développement et la maturation des fruits. Deux paralogues, SlARF2A et SlARF2B, ont été identifiés dans le génome de la tomate. Nous avons montré que l’expression de SlARF2A dans le fruit est régulée par l'éthylène tandis que celle de SlARF2B est induite par l'auxine. La sous-expression de SlARF2A, comme celle de SlARF2B, entraine un retard de maturation alors que l’inhibition simultanée des deux paralogues conduit à une inhibition plus sévère de la maturation suggérant une redondance fonctionnelle entre les deux paralogues lors de la maturation des fruits. Les fruits présentant une sous-expression des gènes SlARF2 produisent de faibles quantités d'éthylène, montrent une faible accumulation de pigments et une plus grande fermeté. Le traitement avec de l'éthylène exogène ne peut pas inverser les phénotypes de défaut de maturation suggérant que SlARF2 pourrait agir en aval de la voie de signalisation de l'éthylène. L'expression des gènes clés de biosynthèse et de signalisation de l'éthylène est fortement perturbée dans les lignées sous-exprimant SlARF2 et les gènes majeurs qui contrôlent le processus de maturation (RIN, CNR, NOR, TAGL1) sont sensiblement sous-régulés. Les données suggèrent que SlARF2 est essentiel pour la maturation des fruits et qu’il pourrait agir au croisement des voies de signalisation de l'auxine et de l'éthylène. Dans le but de mieux comprendre les mécanismes moléculaires par lesquels les ARF régulent l'expression des gènes de réponse à l'auxine, nous avons étudié l'interaction des SlARFs avec des partenaires protéiques ciblés, principalement les co-répresseurs de type Aux/IAA et Topless (TPL) décrits comme les acteurs clés dans la répression des gènes dépendant de la signalisation auxinique. Une fois les gènes codant pour les membres de la famille TPL de tomate isolés, une approche double hybride dans la levure a permis d’établir des cartes exhaustives d'interactions protéine-protéine entre les membres des ARFs et des Aux/IAA d’une part et les ARFs et les TPL d’autre part. L'étude a révélé que les Aux/IAA interagissent préférentiellement avec les SlARF activateurs et qu’à l’inverse les Sl-TPL interagissent uniquement avec les SlARF répresseurs. Les données favorisent l'hypothèse que les ARF activateurs recrutent les Sl-TPL via leur interaction avec les Aux/IAA, tandis que les ARF répresseurs peuvent interagir directement avec les Sl-TPL. Les études d’interactions ont permis également d’identifier de nouveaux partenaires comme les protéines VRN5 et LHP1, composantes des complexes Polycomb PRC impliqués dans la repression par voie épigénétique de la transcription par modification de l'état de méthylation des histones. Au total, le travail de thèse apporte un nouvel éclairage sur le rôle et les mécanismes d'action des ARF et identifie SlARF2 comme un nouvel élément du réseau de régulation contrôlant le processus de maturation des fruits chez la tomate. / The plant hormone auxin coordinates plant development through the regulation of a specific set of auxin-regulated genes and Auxin Response Factors (ARFs) are transcriptional regulators modulating the expression of auxin-response genes. Recent data demonstrated that members of this gene family are able to regulate fruit set and fruit ripening. ARFs are known to act in concert with Aux/IAA to control auxin-dependent transcriptional activity of target genes. However, little is known about other partners of ARFs. The main objective of the thesis research project was to gain more insight on the involvement of ARFs in fruit development and ripening and to uncover their interaction with other protein partners beside Aux/IAAs. Mining the tomato expression databases publicly available revealed that among all tomato ARFs, SlARF2 displays the highest expression levels in fruit with a marked ripening-associated pattern of expression. This prompted us to uncover the physiological significance of SlARF2 and in particular to investigate its role in fruit development and ripening. Two paralogs, SlARF2A and SlARF2B, were identified in the tomato genome and transactivation assay in a single cell system revealed that the two SlARF2 proteins are nuclear localized and act as repressors of auxin-responsive genes. In fruit tissues, SlARF2A is ethylene-regulated while SlARF2B is auxin-induced. Knock-down of SlARF2A or SlARF2B results in altered ripening with spiky fruit phenotype, whereas simultaneous down-regulation of SlARF2A and SlARF2B leads to more severe ripening inhibition suggesting a functional redundancy among the two SlARF2 paralogs during fruit ripening. Double knock-down fruits produce less climacteric ethylene and show delayed pigment accumulation and higher firmness. Exogenous ethylene treatment cannot reverse the ripening defect phenotypes suggesting that SlARF2 may act downstream of ethylene signaling. The expression of key ethylene biosynthesis and signaling genes is dramatically disturbed in SlARF2 down-regulated fruit and major regulators of the ripening process, like RIN, CNR, NOR, TAGL1, are under-expressed. The data support the notion that SlARF2 is instrumental to fruit ripening and may act at the crossroads of auxin and ethylene signaling. Altogether, while ethylene is known as a key hormone of climacteric fruit ripening, the ripening phenotypes associated with SlARF2 down-regulation bring unprecedented evidence supporting the role of auxin in the control of this developmental process. To further extend our knowledge of the molecular mechanism by which ARFs regulate the expression of auxin-responsive genes we sought to investigate interactions SlARF and putative partners, mainly Aux/IAAs and Topless co-reppressors (TPLs) reported to be key players in gene repression dependent on auxin signaling. To this end, genes encoding all members of the tomato TPL family were isolated and using a yeast-two-hybrid approach comprehensive protein-protein interaction maps were constructed. The study revealed that Aux/IAA interact preferentially with activator SlARFs while Sl-TPLs interact only with repressor SlARFs. The data support the hypothesis that activator ARFs recruit Sl-TPLs co-repressors via Aux/IAAs as intermediates, while repressor ARFs can physically interact with Sl-TPLs. Further investigation indicated that SlARFs and Sl-TPLs can interact with polycomb complex PRC1 PRC2 components, VRN5 and LHP1, known to be essential players of epigenetic repression of gene transcription through the modification of histones methylation status. These data establish a potential link between ARFs and epigenetic regulation and thereby open new and original perspectives in understanding the mode of action of ARFs. Altogether, the thesis work provides new insight on the role of ARFs and their underlying action mechanisms, and defines SlARF2 as a new component of the regulatory network controlling the ripening process in tomato.

Le travail de thèse consistera à explorer et caractériser l'ensemble conformationnel de protéines intrinsèquement désordonnées (IDPs) en utilisant plusieurs techniques complémentaires, notamment des simulations avancées de dynamique moléculaire et la diffusion des rayons X aux petits angles (SAXS). Les IDPs sont des protéines possédant une ou plusieurs régions n'ayant pas de structures secondaires stables lorsqu'elles sont isolées, mais pouvant en adopter lors de leur association avec de multiples autres protéines. La question, à laquelle ce travail souhaite répondre dans le cas de trois IDPs, est de savoir si ces éléments de structures secondaires, formés à l'interfaces des complexes protéine-protéine, pré-existent de façon transitoire, ou non, à l'état non-lié des IDPs en solution. S'il est possible d'identifier et de caractériser ces éléments de reconnaissance moléculaire dans les IDPs isolées, alors les résultats de ce travail permettront de guider par la suite la détermination des structures de complexes protéiques impliquant des IDPs. / The PhD work will consist in exploring and characterizing the conformational ensemble of intrinsically disordered proteins (IDPs), by using several complementary methods, including enhanced molecular dynamics simulations and small angle X-ray scattering (SAXS). IDPs are proteins having one or several regions that lack stable secondary structures in the unbound state, but which can adopt various structured conformations to bind other proteins. In the case of three IDPs, the project aims to answer the question of whether these secondary structures formed at the protein-protein interfaces transiently pre-exist or not in the unbound state of solvated IDPs. If it is possible to identify and characterize these molecular recognition features (MoRFs) in the IDP unbound state, then the results of this work will subsequently help to determine the structures of protein complexes involving IDPs.

Proteins are considered the primary actors in living organisms. Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. PPI identification has been addressed by various experimental methods such as the yeast two-hybrid, mass spectrometry, and protein microarrays, to mention a few. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. Therefore a sequence-based framework called ProtInteract is developed to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequential pattern by extracting uncorrelated attributes and more expressive descriptors. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction. Three different scenarios formulate the prediction task. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The present study makes two significant contributions to the field of protein-protein interaction (PPI) prediction. Firstly, it addresses the computational challenges posed by the high dimensionality of protein datasets through the use of dimensionality reduction techniques, which extract highly informative sequence attributes. Secondly, the proposed framework, ProtInteract, utilises this information to identify the interaction characteristics of a protein based on its amino acid configuration. ProtInteract encodes the protein's primary structure into a lower-dimensional vector space, thereby reducing the computational complexity of PPI prediction. Our results provide evidence of the proposed framework's accuracy and efficiency in predicting protein-protein interactions.