The cytochrome bc1 complex of the mitochondrial electron transport chain accomplishes the enzymatic reaction known as the modified Q-cycle. In the Q-cycle the bc1 complex transports protons from the matrix to the intermembrane space of the mitochondria, creating the proton gradient used to make ATP. The energy to move these protons is obtained by shuttling electrons from the coenzyme ubiquinol (QH2) to coenzyme ubiquinone (Q) and the mobile cytochrome c. This well studied complex is ideal for examining molecular adaptation because it consists of ten different subunits, it functions as a dimer, and it includes at least five different active sites. The program TreeSAAP was used to characterize molecular adaptation in the bc1 complex and identify specific amino acid sites that experienced positive destabilizing (radical) selection. Using this information and three-dimensional structures of the protein complex, selection was characterized in terms of coevolution and coadaptation. Coevolution is described as reciprocal local biochemical shifts based on phylogenetic location and results in overall maintenance. Coadaptation, on the other hand, is more dynamic and is described as coordinated local biochemical shifts based on phylogenetic location which results in overall adaptation. In this study both coevolution and coadaptation were identified in various locations on the protein complex near the active sites. Sites in the pore region of cyt c1 were shown to exhibit coevolution, in other words maintenance, of many biochemical properties, whereas sites on helix H of cyt b, which flanks the active sites Qo and Qi, were shown to exhibit coadaptation, in other words coordinated shifts in the specific properties equilibrium constant and solvent accessible reduction ratio. Also, different domains of the protein exhibited significant shifts in drastically different amino acid properties: the protein imbedded in the membrane demonstrated shifts in mainly functional properties, while the part of the complex in the intermembrane space demonstrated shifts in conformational, structural, and energetic properties.

Grâce aux progrès des techniques de séquençage, les bases de données génomiques ont connu une croissance exponentielle depuis la fin des années 1990. Un grand nombre d'outils statistiques ont été développés à l'interface entre bioinformatique, apprentissage automatique et physique statistique, dans le but d'extraire de l'information de ce déluge de données. Plusieurs approches de physique statistique ont été récemment introduites dans le contexte précis de la modélisation de séquences de protéines, dont l'analyse en couplages directs. Cette méthode d'inférence statistique globale fondée sur le principe d'entropie maximale, s'est récemment montrée d'une efficacité redoutable pour prédire la structure tridimensionnelle de protéines, à partir de considérations purement statistiques.Dans cette thèse, nous présentons les méthodes d'inférence en question, et encouragés par leur succès, explorons d'autres domaines complexes dans lesquels elles pourraient être appliquées, comme la détection d'homologies. Contrairement à la prédiction des contacts entre résidus qui se limite à une information topologique sur le réseau d'interactions, ces nouveaux champs d'application exigent des considérations énergétiques globales et donc un modèle plus quantitatif et détaillé. À travers une étude approfondie sur des donnéesartificielles et biologiques, nous proposons une meilleure interpretation des paramètres centraux de ces méthodes d'inférence, jusqu'ici mal compris, notamment dans le cas d'un échantillonnage limité. Enfin, nous présentons une nouvelle procédure plus précise d'inférence de modèles génératifs, qui mène à des avancées importantes pour des données réelles en quantité limitée. / Over the last decades, genomic databases have grown exponentially in size thanks to the constant progress of modern DNA sequencing. A large variety of statistical tools have been developed, at the interface between bioinformatics, machine learning, and statistical physics, to extract information from these ever increasing datasets. In the specific context of protein sequence data, several approaches have been recently introduced by statistical physicists, such as direct-coupling analysis, a global statistical inference method based on the maximum-entropy principle, that has proven to be extremely effective in predicting the three-dimensional structure of proteins from purely statistical considerations.In this dissertation, we review the relevant inference methods and, encouraged by their success, discuss their extension to other challenging fields, such as sequence folding prediction and homology detection. Contrary to residue-residue contact prediction, which relies on an intrinsically topological information about the network of interactions, these fields require global energetic considerations and therefore a more quantitative and detailed model. Through an extensive study on both artificial and biological data, we provide a better interpretation of the central inferred parameters, up to now poorly understood, especially in the limited sampling regime. Finally, we present a new and more precise procedure for the inference of generative models, which leads to further improvements on real, finitely sampled data.