Porométrie liquide-liquide, évaporométrie et simulations sur réseau de pores.

Les milieux nanoporeux (milieux dont les tailles de pores sont submicroniques) sont des objets de haute technologie utilisés dans les techniques de séparation ou encore dans les piles à combustible. La distribution de taille de pores (PSD) de ces milieux, en particulier quand ils sont minces, est une information cruciale pour les applications concernées. La porométrie fluide-fluide et l’évapoporométrie sont deux techniques intéressantes de mise en oeuvre relativement simple visant à obtenir cette information. Les deux méthodes exploitent les propriétés de capillarité des milieux qui contrôlent la hiérarchie dans les classes de pores selon laquelle se fait l’invasion progressive du milieu lors d’un déplacement fluide-fluide immiscible (cas de la porométrie fluidefluide) ou le séchage du milieu (cas de l’évapoporométrie). Toutefois, l’exploitation des données obtenues (seuil de pression versus débit ou masse évaporée versus humidité relative) passe par un modèle pour obtenir les données visées. Les modèles actuellement utilisées considèrent le milieu poreux comme un faisceau de tubes parallèles. Ceci convient parfaitement à certains types de membranes poreuses utilisées en filtration par exemple où la nanostructure est similaire à un arrangement de tubes parallèles. En revanche, beaucoup d’autres milieux présentent des nanostructures enchevêtrées considérablement plus complexes, rendant très critiquables ou pour le moins douteuses l’exploitation des données expérimentales selon le modèle de tubes parallèles. Dans ce contexte, l’objectif de la thèse est de développer des modèles et des simulations numériques permettant d’assoir l’exploitation des données des deux techniques de porométrie sur une base plus solide dans le cas des milieux à nanostructures complexes. L’idée est de s’appuyer sur des modélisations de type réseau de pores des processus concernés : évaporation ou déplacement fluide-fluide immiscible en milieu nanoporeux. Les méthodes réseau de pores s’appuient sur des représentations simplifiées de l’espace des pores permettant des calculs rapides (comparé à des méthodes de calcul directes) tout en prenant en compte les propriétés morphologiques de la nanostructure. Ces méthodes sont utilisées pour l’étude des écoulements diphasiques en milieu poreux. Une première étape consistera à réaliser la simulation de drainage pour la porométrie fluide-fluide et la simulation de séchage pour la technique d'évapoporométrie. Cela revient à simuler, à l'aide de la méthode des réseaux poreux, les écoulements diphasiques qui ont lieu lors de l'utilisation de chacune des deux techniques. Nous évaluerons le modèle de tubes parallèles considéré dans l'analyse par le poromètre fluide-fluide ainsi que les résultats obtenus par l'évapoporomètre. Dans un second temps, nous tenterons de proposer un algorithme d'optimisation pour obtenir la PSD en considérant le modèle de réseau de pores adaptable à différentes classes de nanostructures (milieux fibreux, assemblages de particules sphériques, etc.). Enfin, nous exploiterons l'algorithme d'optimisation pour déterminer la fraction de mouillabilité d'un milieu avec une caractéristique de mouillabilité mixte en utilisant les informations qui peuvent être recueillies à partir du poromètre fluide-fluide

Protein Structure Prediction

Práce popisuje prostorovou strukturu molekul bílkovin a databází uchovávajících representace této struktury, či její hierarchické klasifikace. Je poskytnut přehled současných metod výpočetní predikce struktury bílkovin, přičemž největší pozornost je soustředěna na komparativní modelování. Tato metoda je rovněž v základní podobě implementována a na závěr její implementace analyzována.This work describes the three dimensional structure of protein molecules and biological databases used to store information about this structure or its hierarchical classification. Current methods of computational structure prediction are overviewed with an emphasis on comparative modeling. This particular method is also implemented in a proof-of-concept program and finally, the implementation is analysed.

Long Proteins with Unique Optimal Foldings in the H-P Model

It is widely accepted that (1) the natural or folded state of proteins is a global energy minimum, and (2) in most cases proteins fold to a unique state determined by their amino acid sequence. The H-P (hydrophobic-hydrophilic) model is a simple combinatorial model designed to answer qualitative questions about the protein folding process. In this paper we consider a problem suggested by Brian Hayes in 1998: what proteins in the two-dimensional H-P model have unique optimal (minimum energy) foldings? In particular, we prove that there are closed chains of monomers (amino acids) with this property for all (even) lengths; and that there are open monomer chains with this property for all lengths divisible by four.Comment: 22 pages, 18 figure

Protein structure, distribution of homoplasy and phylogenetic inference

All molecular phylogenetic methods are based on two assumptions: independence of characters and constant selective pressure. It is well known that these assumptions are often violated, but it is assumed that the methods are robust to such violations. However, the increase of unorthodox phylogenies makes us wonder about their reliability. None of the currently used phylogenetic methods and models account for a change of functional/structural constraint at the protein level. In order to understand the consequences on the phylogenetic inference, I studied the distribution of the structural/functional misleading phylogenetic signal in the tree and described the effect of change of constraint at the protein level on the DNA substitution pattern.;While randomly distributed, homoplastic changes cancel each other and can be considered as background noise. However, homoplastic events due to independent change in hydrophobic constraint tend to accumulate in some parts of the tree and can be misleading. A method has been implemented (DISECT) to estimate the distribution of such homoplasies and to improve the tree inference with a partial down-weighting scheme.;Change of constraint at the protein level can influence the DNA substitution pattern. The software program DRUIDS has been designed to detect anomalies in substitution pattern due to a change of constraint. Analysis of four cytochrome b datasets shows that the same regions exhibit a deviation from stationarity in amino acid hydrophobicity and volume and have a high substitution rate. In addition, these regions fail the HKY model of evolution and cluster in areas that interact with neighboring proteins in the bc1 complex. Local constraints change frequently in interacting areas and compensatory substitutions occur to maintain the fitness of the bc1 complex phenotype. Similar study of a protein that interacts with cytochrome b (Subunit 7) confirms these results. A close look at the genetic code structure shows an asymmetry of nucleotide substitutions when there is a change of residue hydrophobicity and volume and current molecular model of evolution do not accommodate such biases.;The results of this dissertation suggest that molecular phylogenetic models and methods should account for more biology to be more reliable

Theoretical Aspects of Designing New Vaccines for Breast Cancer: Docking Studies of Peptide/HLA-A2.1 Complexes

HER2/neu is a transmembrane glycoprotein that is overexpressed in many tumors, including ovarian and breast cancers. The HER2/neu peptide IISAVVGIL (GP2) is recognized by tumor-specific cytotoxic T lymphocytes in the context of human class I major histocompatibility complex (MHC) HLA-A2.1. One limiting-factor for using GP2 as a tumor vaccine is its poor affinity for HLA-A2.1, even though it has the correct peptide-binding motif. The research aims are to develop an accurate docking method for the binding of GP2 to HLA-A2.1, to understand the molecular forces that give rise to strong binding, and to predict mutations that lead to new tumor vaccines. AutoDock and GOLD have been used for docking calculations. The binding free energies from AutoDock correlate qualitatively with experiment. The docked structures for 14 ligands from Autodock3 are in good agreement with experiment. However, the ligands are not fully flexible. GOLD allowed full flexibility to reproduce experimental GP2 structure

Development of genetic algorithm for optimisation of predicted membrane protein structures

Due to the inherent problems with their structural elucidation in the laboratory, the computational prediction of membrane protein structure is an essential step toward understanding the function of these leading targets for drug discovery. In this work, the development of a genetic algorithm technique is described that is able to generate predictive 3D structures of membrane proteins in an ab initio fashion that possess high stability and similarity to the native structure. This is accomplished through optimisation of the distances between TM regions and the end-on rotation of each TM helix. The starting point for the genetic algorithm is from the model of general TM region arrangement predicted using the TMRelate program. From these approximate starting coordinates, the TMBuilder program is used to generate the helical backbone 3D coordinates. The amino acid side chains are constructed using the MaxSprout algorithm. The genetic algorithm is designed to represent a TM protein structure by encoding each alpha carbon atom starting position, the starting atom of the initial residue of each helix, and operates by manipulating these starting positions. To evaluate each predicted structure, the SwissPDBViewer software (incorporating the GROMOS force field software) is employed to calculate the free potential energy. For the first time, a GA has been successfully applied to the problem of predicting membrane protein structure. Comparison between newly predicted structures (tests) and the native structure (control) indicate that the developed GA approach represents an efficient and fast method for refinement of predicted TM protein structures. Further enhancement of the performance of the GA allows the TMGA system to generate predictive structures with comparable energetic stability and reasonable structural similarity to the native structure

Development and Application of Pseudoreceptor Modeling

Quantitative Structure-Activity Relationship (QSAR) methods are a commonly used tool in the drug discovery process. These methods attempt to form a statistical model that relates descriptor properties of a ligand to the activity of that ligand compound towards a specific desired physiological response. QSAR methods are known as a ligand-based method, as they specifically use information from ligands and not protein structural data. However, a derivation of QSAR methods are pseudoreceptor methods. Pseudoreceptor methods go beyond standard QSAR by building a model representation of the protein pocket. However, the ability of pseudoreceptors to accurately replicate natural protein surfaces has not been studied. The goal of this thesis work is to investigate the necessary descriptors to map a protein binding pocket and a method to accurately recreate the 3-D spatial structure of the binding pocket. In addition, additional applications of existing pseudoreceptor methods are explored

Stochastic modelling of bacterial dynamics : adhesion & range expansion

Bacteria, as one of the three domains in the tree of life, play an important role in many phenomena such as biocorrosion, pipe clogging and infections. Since treatment with antibiotics or mechanical removal can be difficult, it is paramount to understand the initial attachment to a substrate and the subsequent colony growth. To this end, this thesis investigates first the adhesion process of Staphylococcus aureus by Monte Carlo simulations and helps to reveal that the bacterium uses different binding mechanisms on hydrophobic and hydrophilic substrates. On hydrophobic substrates, the bacterium's macromolecules bind freely. Subsequently, the bacteria show large cell-to-cell variation in adhesion forces but only small variations by repetitions with the same cell. On hydrophilic substrates, the macromolecules need to overcome a potential barrier. This leads to a comparable variability between repetitions with the same cell and the cell-to-cell variance. As a second model system, I investigated the competitive range expansion of microbial colonies with heterogeneous mechanical interactions by stochastic simulations. This is, for instance, realised by a network of piliated bacteria such as Neisseria gonorrhoeae. The simulations reveal that a heterogeneous susceptibility to division generated pushing significantly affects the competition dynamics of growing bacteria. Furthermore, homogeneous pushing leads to a small but standing variation of a disadvantaged subpopulation inside the expanding colony.Bakterien spielen in vielen Lebensbereichen wie beispielsweise Biokorrosion, Verstopfung von Rohren oder Infektionen eine zentrale Rolle. Da sich ihre Entfernung mittels Antibiotika oder mechanischer Methoden als schwierig erweisen kann, ist es wichtig die initiale Adhäsion und das nachfolgende Wachstum grundlegend zu verstehen. Zuerst wurde das Adhäsionsverhalten von Staphylococcus aureus auf hydrophilen und hydrophoben Oberflächen mittels Monte Carlo Simulationen untersucht. Es zeigt sich, dass die Adhäsion durch Makromoleküle der Zellwand vermittelt wird, von denen viele auf hydrophoben Oberflächen binden können. Dies resultiert in einer hohen Variabilität der Adhäsionskräfte innerhalb der Population, während Wiederholungen mit derselben Zelle ähnlich verlaufen. Auf hydrophilen Oberflächen selektiert jedoch eine Potentialbarriere stochastisch die bindenden Makromoleküle. Dies führt zu einer vergleichbaren Variabilität der Adhäsionskräfte zwischen Wiederholungen mit derselben Zelle und der Gesamtpopulation. Als zweites Modellsystem betrachtete ich das kompetitivem Koloniewachstum von pilierten Neisseria gonorrhoeae Bakterien. Zur Beschreibung dieser entwickelte ich ein minimales stochastisches Model von kompetitivem Koloniewachstum mit heterogenen mechanischen Wechselwirkungen. Die Simulationen zeigen, dass eine heterogene Suszeptibilität der Bakterien gegenüber Kräften, welche durch Zellteilung generiert werden, das kompetitive Wachstum maßgeblich beeinflussen