En infectiologie, comme en cancérologie, la médecine personnalisée se développe. Ainsi, les traitements antibiotiques probabilistes cèdent leur place à des traitements adaptés aux pathologies et aux patients. En plus des risques d’échecs liés à une thérapie non adaptée, le traitement probabiliste d’une infection est associé à l’augmentation des résistances acquises chez les bactéries. Cependant, cette orientation nécessite de disposer de tests compagnons, c’est-à-dire des tests diagnostiques sensibles et spécifiques pouvant précocement identifier les bactéries et les marqueurs de résistance à partir des liquides biologiques. A côté des méthodes moléculaires largement développées mais ayant des limites de multiplexage, les techniques protéomiques ont récemment été intégrées dans le diagnostic en infectiologie. Ce travail de thèse a consisté à développer des méthodes de spéctrométrie de masse et à les appliquer à la détection de pathogènes et de marqueurs de résistance. Ce travail s’est focalisé sur trois applications : 1) l’identification, la caractérisation et la quantification précoce de micro-organismes dans des échantillons primaires (aspirats endotrachéaux (AET)) de patients atteints de pneumopathies acquises sous ventilation mécanique (PAVM), 2) la détection d’éléments génétiques de la résistance aux antibiotiques : les intégrons, 3) la détection de phénotypes de résistance aux antibiotiques chez Staphylococcus aureus. / Personalized medicine for infectious diseases or cancer becomes more and more important in modern therapy. Furthermore, probabilistic treatment has been associated with the development of resistant bacteria causing infectious diseases. As a result, probabilistic treatments are replaced by adapted treatment for pathologies and patients. However, this new approach needs available companion diagnosis tests that are sensitive but also specific tests able to provide rapid pathogen and resistance markers identification in biological fluids. Beside molecular methods, widely developed but with multiplex limits, proteomic technics have recently joined the infectious diagnosis. This work consisted in developing mass spectrometry technics for bacteria and resistance marker identifications. This work focused on 3 applications: 1) identification and quantitation of microorganisms in crude samples (endotracheal aspirates (ETA)) from patient suffering of ventilator associated pneumonia (VAP), 2) detection of genetic elements involved in antibiotic resistance : the integrons, 3) detection of antibiotic resistance phenotypes in S. aureus.

Accurate predictions of binding free energies from computer simulations are an invaluable resource for understanding biochemical processes and drug action. The primary aim of the work described in the thesis was to predict and understand ligand binding to several proteins of major pharmaceutical importance using computational methods. We report a computational strategy to quantitatively predict the effects of alanine scanning and ligand modifications based on molecular dynamics free energy simulations. A smooth stepwise scheme for free energy perturbation calculations is derived and applied to a series of thirteen alanine mutations of the human neuropeptide Y1 G-protein coupled receptor and a series of eight analogous antagonists. The robustness and accuracy of the method enables univocal interpretation of existing mutagenesis and binding data. We show how these calculations can be used to validate structural models and demonstrate their ability to discriminate against suboptimal ones. Site-directed mutagenesis, homology modelling and docking were further used to characterize agonist binding to the human neuropeptide Y2 receptor, which is important in feeding behavior and an obesity drug target.  In a separate project, homology modelling was also used for rationalization of mutagenesis data for an integron integrase involved in antibiotic resistance. Blockade of the hERG potassium channel by various drug-like compounds, potentially causing serious cardiac side effects, is a major problem in drug development. We have used a homology model of hERG to conduct molecular docking experiments with a series of channel blockers, followed by molecular dynamics simulations of the complexes and evaluation of binding free energies with the linear interaction energy method. The calculations are in good agreement with experimental binding affinities and allow for a rationalization of three-dimensional structure-activity relationships with implications for design of new compounds. Docking, scoring, molecular dynamics, and the linear interaction energy method were also used to predict binding modes and affinities for a large set of inhibitors to HIV-1 reverse transcriptase. Good agreement with experiment was found and the work provides a validation of the methodology as a powerful tool in structure-based drug design. It is also easily scalable for higher throughput of compounds.