Molecular interactions lie at the heart of myriad biological processes. Knowledge of molecular recognition processes and the ability to model and predict interactions of any biological molecule to any chemical compound are the key for better understanding of cell functions and discovery of more efficacious medicines. This thesis presents contributions to the development of a novel chemo-bioinformatics approach called proteochemometrics; a general method for interaction space analysis of biological macromolecules and their ligands. In this work we explore proteochemometrics-based interaction models over broad groups of protein families, evaluate their validity and scope, and compare proteochemometrics to traditional modeling approaches. Through the proteochemometric analysis of large interaction data sets of multiple retroviral proteases from various viral species we investigate complex mechanisms of drug resistance in HIV-1 and discover general physicochemical determinants of substrate cleavage efficiency and binding in retroviral proteases. We further demonstrate how global proteochemometric models can be used for design of protease inhibitors with broad activity on drug-resistant viral mutants, for monitoring drug resistance mechanisms in the physicochemical sense and prediction of potential HIV-1 evolution trajectories. We provide novel insights into the complexity of HIV-1 protease specificity by constructing a generalized IF-THEN rule model based on bioinformatics analysis of the largest set of HIV-1 protease substrates and non-substrates. We discuss how proteochemometrics can be used to map recognition sites of entire protein families in great detail and demonstrate how it can incorporate target variability into drug discovery process. Finally, we assess the utility of the proteochemometric approach in evaluation of ADMET properties of drug candidates with a special focus on inhibition of cytochrome P450 enzymes and investigate application of the approach in the pharmacogenomics field.

Comprendre les interactions réalisées entre un candidat médicament et sa protéine cible est un enjeu crucial pour orienter la recherche de nouvelles molécules. En effet, ce processus implique de nombreux paramètres qu’il est nécessaire d’analyser séparément pour mieux comprendre leurs effets.Nous proposons ici deux nouvelles approches observant les relations protéine/ligand. La première se concentre sur la comparaison de cavités formées par les sites de liaison pouvant accueillir une molécule. Cette méthode permet d’inférer la fonction d’une protéine mais surtout de prédire « l’accessibilité » d’un site de liaison pour un médicament. La seconde tactique se focalise sur la comparaison des interactions non-covalentes réalisées entre la protéine et le ligand afin d’améliorer la sélection de molécules potentiellement actives lors de criblages virtuels, et de rechercher de nouveaux fragments moléculaires, structuralement différents mais partageant le même mode d’interaction. / Understanding the interactions between a drug and its target protein is crucial in order to guide drug discovery. Indeed, this process involves many parameters that need to be analyzed separately to better understand their effects.We propose two new approaches to observe protein/ligand relationships. The first focuses on the comparison of cavities formed by binding sites that can accommodate a small molecule. This method allows to infer the function of a protein but also to predict the accessibility of a binding site for a drug. The second method focuses on the comparison of non-covalent interactions made between the protein and the ligand to improve the selection of potentially active molecules in virtual screening, and to find new molecular fragments, structurally different but sharing the same mode of interaction.

Prédire, modéliser et analyser le métabolisme de xénobiotiques, substances étrangères à un organisme, à l'aide de méthodes informatiques est un challenge majeur mobilisant la communauté scientifique depuis de nombreuses années. Cette thèse vise à implémenter des méthodes informatiques multi-échelles pour prédire et analyser le métabolisme des xénobiotiques. Un premier axe de cette étude portait sur la construction et l'annotation automatique de novo de graphes métaboliques combinant fortes sensibilités et précisions. Ces graphes fournissent ainsi la prédiction du métabolisme de xénobiotiques chez l'homme, ainsi que la génotoxicité des molécules et atomes qui le composent. Puis, le travail s'est orienté sur l'implémentation d'un modèle mathématique dynamique modélisant des effets de compétition enzymatique à travers le développement d'une méthodologie permettant l'exploitation de données biologiques restreintes tout en limitant les biais inhérents. / Predicting, modelling and analysing the metabolism of xenobiotics, substances foreign to an organism, using computer methods, has been a major challenge for the scientific community for many years. This thesis aims to implement multiscale computing methods for predicting and analyzing the metabolism of xenobiotics. A first focus of this study was on the construction and automatic de novo annotation of metabolic graphs combining high sensitivity and precision. These graphs thus provide the prediction of the metabolism of xenobiotics in humans, as well as the genotoxicity of the molecules and atoms that make up xenobiotics. Then, the work focused on the implementation of a dynamic mathematical model modelling enzymatic competition effects through the development of a methodology allowing the exploitation of limited biological data while limiting inherent biases.

No description available.

Microbial natural products have been a source for new drugs for many decades and are unrivaled in their capacity to generate not only future therapeutic agents, but also providing key agents for agricultural and industrial use. LC-MS/MS based metabolomic tools and technologies have been developed that can rapidly dereplicate nonribosomal peptides and statistically identify related congeners in an automated nontargeted process from complex natural product extracts with nanogram sensitivity. This data-base search approach is designed to handle linear, cyclic and cyclic-branched nonribosomal peptides from proteinogenic and nonproteinogenic amino acids without genomic data or traditional bioactivity directed fractionation. Chemometric work-flows combined with a comprehensive metabolomic guided discovery strategy were used to profile the chemical space of a diverse collection of understudied fungal endophytes from fruiting plants. This approach allowed for the prioritization of unique isolates and for the focused discovery, isolation and characterization of distinct outlier metabolites by LC-SPE, 1D and 2D NMR, HRMS and single crystal X-ray analysis. These metabolomic tools and strategies have led to the discovery and characterization of 35 new and over 40 known natural products, many of which are biologically active. This thesis with enabling metabolomic tools and novel discoveries has demonstrated the utility of these analytical methodologies as an effective strategy for the untargeted discovery of new natural products from bacteria and endophytic fungi. / Thesis / Doctor of Philosophy (PhD)

La découverte de motifs est une tâche importante en fouille de données. Cemémoire traite de l’extraction des motifs émergents minimaux. Nous proposons une nouvelleméthode efficace qui permet d’extraire les motifs émergents minimaux sans ou avec contraintede support ; contrairement aux méthodes existantes qui extraient généralement les motifs émergentsminimaux les plus supportés, au risque de passer à côté de motifs très intéressants maispeu supportés par les données. De plus, notre méthode prend en compte l’absence d’attributqui apporte une nouvelle connaissance intéressante.En considérant les règles associées aux motifs émergents avec un support élevé comme desrègles prototypes, on a montré expérimentalement que cet ensemble de règles possède unebonne confiance sur les objets couverts mais malheureusement ne couvre pas une bonne partiedes objets ; ce qui constitue un frein pour leur usage en classification. Nous proposons uneméthode de sélection à base de prototypes qui améliore la couverture de l’ensemble des règlesprototypes sans pour autant dégrader leur confiance. Au vu des résultats encourageants obtenus,nous appliquons cette méthode de sélection sur un jeu de données chimique ayant rapport àl’environnement aquatique : Aquatox. Cela permet ainsi aux chimistes, dans un contexte declassification, de mieux expliquer la classification des molécules, qui sans cette méthode desélection serait prédites par l’usage d’une règle par défaut. / Pattern discovery is an important field of Knowledge Discovery in Databases.This work deals with the extraction of minimal emerging patterns. We propose a new efficientmethod which allows to extract the minimal emerging patterns with or without constraint ofsupport ; unlike existing methods that typically extract the most supported minimal emergentpatterns, at the risk of missing interesting but less supported patterns. Moreover, our methodtakes into account the absence of attribute that brings a new interesting knowledge.Considering the rules associated with emerging patterns highly supported as prototype rules,we have experimentally shown that this set of rules has good confidence on the covered objectsbut unfortunately does not cover a significant part of the objects ; which is a disavadntagefor their use in classification. We propose a prototype-based selection method that improvesthe coverage of the set of the prototype rules without a significative loss on their confidence.We apply our prototype-based selection method to a chemical data relating to the aquaticenvironment : Aquatox. In a classification context, it allows chemists to better explain theclassification of molecules, which, without this method of selection, would be predicted by theuse of a default rule.

No description available.

Solubility prediction usually refers to prediction of the intrinsic aqueous solubility, which is the concentration of an unionised molecule in a saturated aqueous solution at thermodynamic equilibrium at a given temperature. Solubility is determined by structural and energetic components emanating from solid-phase structure and packing interactions, solute–solvent interactions, and structural reorganisation in solution. An overview of the most commonly used methods for solubility prediction is given in Chapter 1. In this thesis, we investigate various approaches to solubility prediction and solvation model development, based on informatics and incorporation of empirical and experimental data. These are of a knowledge-based nature, and specifically incorporate information from the Cambridge Structural Database (CSD). A common problem for solubility prediction is the computational cost associated with accurate models. This issue is usually addressed by use of machine learning and regression models, such as the General Solubility Equation (GSE). These types of models are investigated and discussed in Chapter 3, where we evaluate the reliability of the GSE for a set of structures covering a large area of chemical space. We find that molecular descriptors relating to specific atom or functional group counts in the solute molecule almost always appear in improved regression models. In accordance with the findings of Chapter 3, in Chapter 4 we investigate whether radial distribution functions (RDFs) calculated for atoms (defined according to their immediate chemical environment) with water from organic hydrate crystal structures may give a good indication of interactions applicable to the solution phase, and justify this by comparison of our own RDFs to neutron diffraction data for water and ice. We then apply our RDFs to the theory of the Reference Interaction Site Model (RISM) in Chapter 5, and produce novel models for the calculation of Hydration Free Energies (HFEs).