The differences between refolding mechanisms of sperm whale apomyoglobin subsequent to three different unfolding conditions have been examined by atomistic level computer simulations. The three unfolding conditions used in this work are high-temperature, low temperature and low pH. The folding of this protein has been extensively studied experimentally, providing a large data base of folding parameters which can be probed using simulations. The crystal structure of sperm whale myoglobin was taken from Protein Data Bank, followed by the removal of the heme unit and a subsequent energy minimization was performed in order to generate the native apomyoblogin form. Thus, the native conformation of apomyoglobin utilized is the same in all the three different refolding simulations done in the present work. The differences are the way the initial unfolded conformations were obtained. The refolding trajectories were obtained at room temperature using the Stochastic Difference Equation in Length algorithm. The results reveal differences between the three refolding routes. In contrast to previous molecular simulations that modeled low pH denaturation, an extended intermediate with large helical content was not observed in the refolding simulations from the high-temperature unfolded state. Otherwise, a structural collapse occurs without formation of helices or native contacts. Once the protein structure is more compact (radius of gyration less than 18 angstroms) secondary and tertiary structures appear. The low pH simulations show some agreement with the low pH experimental data and previous molecular dynamics simulations, like formation of a conformation having radius of gyration around 20 angstroms and large helical content. And the refolding simulations after the low temperature unfolding present differences in the properties of apomyoglobin folding route, comparing to the other two previous conditions. The collapse of the protein during folding occurs later in the simulation when compared with high-temperature denaturing state, but earlier when compared to low pH simulations. These differences strongly suggest that a protein can follow different folding routes, depending on the nature and the structure of the unfolded state.

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No description available.

Cette thèse présente une approche de modélisation inspirée par la robotique pour l'étude des changements conformationnels des protéines. Cette approche est basée sur une représentation mécanistique des protéines permettant l'application de méthodes efficaces provenant du domaine de la robotique. Elle fournit également une méthode appropriée pour le traitement « gros-grains » des protéines sans perte de détail au niveau atomique. L'approche présentée dans cette thèse est appliquée à deux types de problèmes de simulation moléculaire. Dans le premier, cette approche est utilisée pour améliorer l'échantillonnage de l'espace conformationnel des protéines. Plus précisément, cette approche de modélisation est utilisée pour implémenter des classes de mouvements pour l'échantillonnage, aussi bien connues que nouvelles, ainsi qu'une stratégie d'échantillonnage mixte, dans le contexte de la méthode de Monte Carlo. Les résultats des simulations effectuées sur des protéines ayant des topologies différentes montrent que cette stratégie améliore l'échantillonnage, sans toutefois nécessiter de ressources de calcul supplémentaires. Dans le deuxième type de problèmes abordés ici, l'approche de modélisation mécanistique est utilisée pour implémenter une méthode inspirée par la robotique et appliquée à la simulation de mouvements de grande amplitude dans les protéines. Cette méthode est basée sur la combinaison de l'algorithme RRT (Rapidly-exploring Random Tree) avec l'analyse en modes normaux, qui permet une exploration efficace des espaces de dimension élevée tels les espaces conformationnels des protéines. Les résultats de simulations effectuées sur un ensemble de protéines montrent l'efficacité de la méthode proposée pour l'étude des transitions conformationnelles / Proteins are biological macromolecules that play essential roles in living organisms. Un- derstanding the relationship between protein structure, dynamics and function is indis- pensable for advances in fields such as biology, pharmacology and biotechnology. Study- ing this relationship requires a combination of experimental and computational methods, whose development is the object of very active interdisciplinary research. In such a context, this thesis presents a robotics-inspired modeling approach for studying confor- mational changes in proteins. This approach is based on a mechanistic representation of proteins that enables the application of efficient methods originating from the field of robotics. It also provides an accurate method for coarse-grained treatment of proteins without loosing full-atom details.The presented approach is applied in this thesis to two different molecular simulation problems. First, the approach is used to enhance sampling of the conformational space of proteins using the Monte Carlo method. The modeling approach is used to implement new and known Monte Carlo trial move classes as well as a mixed sampling strategy. Results of simulations performed on proteins with different topologies show that this strategy enhances sampling without demanding higher computational resources. In the second problem tackled in this thesis, the mechanistic modeling approach is used to implement a robotics-inspired method for simulating large amplitude motions in proteins. This method is based on the combination of the Rapidly-exploring Random Tree (RRT) algorithm with Normal Mode Analysis (NMA), which allows efficient exploration of the high dimensional conformational spaces of proteins. Results of simulations performed on ten different proteins of different sizes and topologies show the effectiveness of the proposed method for studying conformational transitions

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No description available.

Pseudomonas aeruginosa (PA) est la troisième bactérie impliquée dans les maladies nosocomiales et est la principale cause de mortalité des patients atteints de la mucoviscidose. PA est résistante à la plupart des traitements antibiotiques. Trouver de nouvelles stratégies thérapeutiques est devenu un enjeu majeur de santé publique, l’une d’entre elles est l’inhibition de facteurs de virulence. Parmi ceux-ci, les lectines sont des protéines impliquées dans l’adhésion et la formation de biofilm via des interactions avec des sucres (PA-IL, PA- IIL, FliC, FliD, PilA, PilY1 et CupB6).Le but de ce travail est donc de trouver des leurres moléculaires ayant une forte affinité pour ces lectines. Ceux-ci sont des motifs saccharidiques présentés de façon multivalente : glycoclusters. De part leur grande diversité structurale et leur faible quantité, un outil de criblage innovant a été développé qui consiste en une lame de verre microstructurée : le glycocluster-microarray. Les glycoclusters sont immobilisés de manière ordonnée par DNA Directed Immobilization (DDI). Deux méthodes de criblage ont été développées grâce à cet outils : 1) le criblage en solution et par compétition d’une bibliothèque de motifs saccharidiques et 2) le criblage d’une bibliothèque de glycoclusters immobilisés sur le microarray. Avec cet outil, des protocoles de mesures d’IC50 et de Kd ont aussi été fiabilisés pour caractériser les meilleurs candidats inhibiteurs des lectines. Le glycocluster- microarray présente l’avantage de n’utiliser qu’une très faible quantité de matériel (quelques picomoles) et permet de réaliser diverses analyses en parallèle.Afin de valider cet outil, une étude sur l’impact de la densité de surface en glycocluster a été menée. Le criblage de plus de 150 motifs saccharidiques a permis de sélectionner ceux ayant une forte affinité pour les lectines. L’analyse sur microarray complétée par de la modélisation moléculaire d’une bibliothèque de glycoclusters, possédant ces motifs et différentes topologies, valences et propriétés (aromaticité, charge,…), a permis d’identifier les paramètres clés dirigeant les relations structure-affinité. Une activité anti-biofilm chez PA a été démontrée avec les meilleurs glycoclusters ciblant PA-IL.Tester l’activité in vivo, chez l’animal, des meilleurs candidats est une voie à explorer. Cibler d’autres lectines comme celles présentes sur le flagelle et les pili de PA et notamment impliquées dans son adhésion précoce est aussi une voie à développer. Pour cela, des tests préliminaires ont été présentés et d’autres sont en cours faisant appel à l’utilisation de bactéries entières ainsi qu’à une détection sans marquage des lectines. / Summary: Pseudomonas aeruginosa (PA) is the third pathogen involved in nosocomial diseases and the major cause of mortality of cystic fibrosis patients. PA develops resistance to antibiotics treatments. And so, developing new therapeutic strategies is a public health issue. One of the promising strategies is to inhibit virulence factors involved in the adhesion and the biofilm formation of PA. Some of these virulence factors are lectins which interact with sugars (PA-IL, PA-IIL, FliC, FliD, PilA, PilY1 and CupB6).The goal of this work is to find molecular decoys which have a strong affinity for these lectins. These are saccharidic units with a multivalent display: glycoclusters. An innovative screening tool has been developed: the glycocluster-microarray, to study lectin/glycocluster interactions. It is a microstructured glass slide where glycoclusters are immobilized by DNA Directed Immobilization (DDI). Two screening methods have been developed with this microarray: 1) the screening in solution and by competition of a saccharidic units library and2) the screening of a glycoclusters library immobilized on the microarray. Protocols of IC50 and Kd measurements have also been developed with this tool to characterize the best lectins inhibitors. This tool allows to use few amount of material (few picomoles) and to do parallel analysis.To validate the microarray, a study of the impact of glycoclusters surface density has been done. The screening of more than 150 saccharidic units allowed the selection of the ones that display the best affinity forlectins. The analysis, on microarray and molecular simulations, of the glycoclusters library displaying thesesaccharidic units and several topologies, valences and properties (aromaticity, charge,…) enable to identify key parameters of structure-affinity relationships. An anti-biofilm activity has been observed for the best glycoclusters targeting PA-IL.Testing in vivo activity of these best candidates will be explored. Targeting others lectins such as the ones on the flagella and pili of PA and involved in the early adhesion needs also to be developed. To this end, preliminary tests have been showed and some are in progress.

This thesis explores the use of computational chemistry and machine learning techniques to aid in the design of Metal-Organic Frameworks (MOFs) for use in postcombustion carbon capture and storage (PoC-CCS). PoC-CCS is an ongoing field of research which aims to selectively remove carbon dioxide, an important greenhouse gas, from the exhaust of fossil-fuel burning powerplants. By using a suite of advanced simulation techniques, high-throughput screenings were performed on thousands of MOFs to study their behaviour in a pressure swing adsorption (PSA) system. To develop a comprehensive picture of a material’s performance, the behaviour of individual gas molecules within the pores of the crystal structures to the material’s performance in industrial scale PSA columns was evaluated. To study the behaviour of individual gas molecules within the pores of a MOF, a new algorithm which can accurately determine the locations of gas binding sites was developed. This algorithm, which relies on probability distributions generated through grand canonical Monte Carlo simulations (GCMC), was optimized for CO2 with the goal of use in high-throughput screening. By tuning the user-controlled parameters for a desired gas, this algorithm, which was named the Guest Atom Localization Algorithm (GALA), was shown to accurately reproduce experimentally determined binding sites while being run in a high-throughput manner with no user intervention. Studying MOFs at the pore or crystal scale in this manner provides valuable insights into the behaviour of gases within the materials. A major shortcoming, however, is the lack of direct insight into the material’s behaviour in industrial systems. Materials scientists and MOF chemists have historically focused on a set of performance metrics measured at this scale; however, no clear connection can be made between such metrics and the performance of that sorbent material in a PSA column. To bridge this gap between MOF chemists and the process engineers studying the PSA systems, a large-scale screening of MOFs was performed using a sophisticated PSA simulator designed to reproduce the performance of an 80 kg PSA column. By supplying isotherms obtained using GCMC simulations to be used as inputs into the PSA simulator, a multi-scale high-throughput screening of MOFs for PoC-CCS was performed for the first time under coal-fired powerplant conditions. This multi-scale screening provided the ideal conditions to study the materials science performance metrics and their relationships to industrial PSA performance. To study this relationship, a series of machine learning and artificial intelligence techniques were employed. The primary goal was to extract important relationships between the materials science and industrial PSA performance metrics, with a secondary goal of developing a predictive model which could be used to accelerate the pace of materials discovery. Through the use of machine learning, several metrics were identified which could be used to predict whether a material could meet the minimum target of 95 % purity of captured CO2, and 90 % removal (or recovery) of CO2 from the flue gas stream. Among them was the isotherm parameters for N2, the most abundant species in the flue gas. This finding was significant as to date the focus among MOF chemists studying the PoC-CCS system was placed primarily on the CO2 metrics, with N2 only implicitly considered when calculating the CO2/N2 selectivity. Although several metrics were identified which could predict the purity and recovery targets, none of the conventional metrics tested could be used to estimate the energetic cost of capture or the size of the capture plant, both important considerations in evaluating the cost of capture. The relationship between N2 binding within the pores of the MOF and its ability to meet the purity-recovery targets was explored using GALA. Using a Tanimoto similarity metric and the ratio of single component and competitive loadings, the CO2 and N2 binding environments were studied. It was determined that when the N2 binding environment was significantly altered by the presence of CO2, the material was more likely to meet the purity-recovery targets. Further analysis found that this change in binding environments was correlated to a reduced N2 uptake in the presence of CO2, implying that the competition for binding sites within the pores of the MOF is an important indicator for the material’s ability to meet the purity-recovery target. For the first time, a direct relationship between the behaviour of individual gas molecules to industrial PSA performance can be reported. Although the PSA simulator used throughout this work has proven to be a powerful tool for materials discovery, several shortcomings still exist. The first is the method used by the simulator to predict the loadings at various points within the column. This method relies on single component isotherm data despite the ability of GCMC to simulate multi-component isotherms. An alternative method to using single component isotherms was proposed which relies on multi-component isotherm data and a linear interpolation model. The existing method was compared to the new proposed interpolation method, and it was found that the loadings predicted using the interpolation method were more accurate. The second shortcoming of the PSA simulator is the computational expense associated with the optimizations. Using the PSA simulator, a single material may take up to a week to be fully optimized on a high-performance computing cluster. To increase the pace of materials discovery, a surrogate model was developed using the data accumulated over the course of the work presented in this thesis. Using artificial neural networks, a suite of models was developed which reproduces the outputs of the PSA simulator and is able to optimize a single MOF in a matter of minutes. This suite of models, known as the Fossil Fuel Combustion for Carbon Capture and Storage (FoCAS) was used to perform a screening of over 4,000 materials.

Molecular dynamics (MD) simulation studies were considered in this study in the fields of phosphonium based ionic liquids (PBILs) and heterogeneous (solid/liquid) zeolite systems. A new generation of ionic liquids (ILs) called phase-separable ionic liquids (PSILs) are able to dissolve cellulose and lignin, a necessary step, for conversion of biomass to fuels and chemicals with co-solvents and are immiscible with water or saline solutions. Molecular simulations on these systems will provide insights of phase behavior and dissolution phenomenon. The knowledge of interfacial phase behavior of ionic liquids/solvent systems is critical for materials discovery for designing efficient dissolution processes. Transition zone from miscible to immiscible behavior was observed for alkyl chain lengths varying from 6 to 8. Emulsion phase was observed for [P8888]+ ion. Result from molecular dynamics (MD) simulations shows excellent agreement with experimental data for both chloride and acetate anions. These contributions will be helpful in modeling PBILs system for cellulose dissolution, liquid-liquid extraction and biomass studies. Another important aspect in biofuel conversion is glucose isomerization step using zeolites. Zeolites are crystalline solids that have wide applications in industrial areas for its hydrocarbon conversion, adsorption of molecules. In this study, we report MD simulation studies on glucose solution diffusion into zeolite structure as a function of temperature and pressure. Development of united-atom force field for PBILs, for phosphonium cation with anions of chloride and acetate, is considered in this study. Force field parameterization was considered for these ionic liquids with a variation of alkyl chain length in phosphonium ion with chloride and acetate anions. Performance of force field parameters was analyzed by calculating properties such as density and viscosity at various temperature and compared with available experimental data. Efficient algorithm techniques were developed in molecular simulations that will reduce computational load in calculating non-bonded interactions. We introduce theory of local sample (TLS) in calculating non-bonded interactions acting on atoms. Another algorithmic improvement in MD simulations is calculating force acting on atoms based on previous time steps, that achieves up to 50 % reduction in computational time

The primary goal of the research studies conducted was to apply statistical mechanical and computer simulation methods to describe the equilibrium behavior of hazardous dipolar/quadru-polar single-gases and mixtures confined in micro porous adsorbents. Statistical mechanical models capable of handling the energetic heterogeneity by complex electrostatic interactions between adsorbate-adsorbent and adsorbate-adsorbate electrostatic interactions were developed and studied. The heterogeneous pore shape and size of different adsorbents were taken into account by two different approaches described in the following paragraphs. Under certain conditions, the use of Mean Field Perturbation Theories (MFPTs) is more attractive than Monte-Carlo (MC) simulations because of the enhanced physical insights that they offer, as well as very low computational times required. Existing literature shows that the applications of MFPTs for studying adsorption of polar molecules were limited due to the orientation dependency of the intermolecular potentials for electrostatic interactions, that in turn poses the challenging problem of seeking analytical expressions for the various thermodynamic functions involved. Furthermore, other existing approaches of accounting for complex electrostatic interactions through hydrogen bonding have limitations due to the requirement of parameter estimation related to radial distribution functions and the critical orientation values of molecules for hydrogen bonds, which are generally obtained through MC simulations and X-ray scattering techniques. In the first stage of research efforts, an attempt was made to express angle-dependent intermolecular potentials in the form of angle-independent intermolecular potential terms by employing statistical averaging methods. In particular, the permanent dipole-dipole and permanent dipole-induced dipole intermolecular potentials were expressed as angle-averaged intermolecular potentials. Then, angle-averaged intermolecular potentials were used to predict water isotherms in nano-slit pores. Furthermore, the angle-averaged intermolecular potentials were used for a binary mixture of polar molecules (water-methanol) to predict the adsorption behavior in nano-slit pores. However, significant limitations of MFPTs arise when they are used for the study of adsorption in zeolites that exhibit irregular shaped cavities with surface heterogeneities. The latter certainly represent a future meaningful research direction. It should be pointed out, that the mean field approach allows us to predict equilibrium sorption properties in homogeneous adsorbents like graphitic carbon (slit), carbon nano tubes (cylinder) and highly siliceous faujasites (spherical) as they have regular shaped cavities. The applications of such kinds of theory remained limited due to the (generally) unknown distribution of functional sites on adsorbents of interests (mainly activated carbons and zeolites) and their locations in the adsorbent framework. The second stage of research efforts focused on models capable of incorporating surface heterogeneities and addressing complex pore geometries. The models developed relied on Grand Canonical Monte-Carlo (GCMC) simulations. In particular, two types of GCMC simulations were carried out, namely molecular and atomistic MC simulations. Both techniques were applied to simulate sorption isotherms on zeolites and activated carbon to remove mercury chloride (quadrupole), hydrogen cyanide (HCN, dipole) and methyl ethyl ketone (MEK, dipole) from air. The molecular based MC technique utilized molecular properties of the molecules namely dipole, quadrupole moments, molecular polarizability and molecule size (kinetic diameter). The molecule was considered to be a spherical shaped particle. The dispersion interactions were calculated using Vaan der Waals equation and electrostatic interactions were quantified through the multi-pole expansion method. This approach was used to simulate adsorption of HgCl2, HCN and MEK in zeolite NaX and activated carbon with functional sites namely carbonyl, hydroxyl and carboxyls. Simulation results indicated that HgCl2 sorption could be attributed to charge-induced dipole interactions for activated carbon, suggesting that sorbents with more number surface charges can be useful except for the case of carbonyls in which quadrupole moments plays a crucial role in reducing sorbent capacities, in turn implying that relative positions of positively and negatively charged cations are indeed important. However, for zeolite NaX, performance characteristics were primarily attributed to charge-quadrupole interactions and dispersion interactions. Moreover, zeolite-NaX performance characteristics for capturing HCN and MEK were attributed to dipole-Na interactions due to the relatively large dipole moments of the molecules under consideration. In the case of activated carbon, HCN sorption was governed by mainly charge-dipole and charge-induced dipole interactions, and hence, carbons with carboxyls seemed to perform better than hydroxyls and carbonyls. MEK sorption was influenced by dispersion interactions (due to the large polarizability of MEK) and charge-dipole interactions, which makes carbon with carbonyls more efficient rather than carbons with hydroxyls having the same charge densities. However, application of the aforementioned molecular approaches was limited to sorbents with regular shape cavities having some surface heterogeneity such as activated carbons. Finally, in order to account for sorbents with irregular shaped cavities, such as silicalite and mordenite, one needs to use atomistic MC simulations. The atomistic MC technique utilizes appropriate atomic sizes and charges for the molecules under consideration to quantify intermolecular forces among the adsorbate molecules and the atoms of the zeolite framework as well as activated carbon. The dispersion interactions were calculated using the Van-Der Waals equation and electrostatic interactions were quantified through a standard Coulombic equation. The bond distances among atoms were kept fixed but variations in angular movement and dihedral/torsional movements were considered, and appropriate harmonic potentials were used to account for angle bending and torsional effects. The sorption performance was evaluated for mordenite, silicalite and zeolite beta for a Si/Al ratio of 47-197 for both an HCN and MEK system. The results of HCN/MEK sorption suggested that silicalite has greater capacity than that of mordenites .In the case of MEK Zeolite beta with sodium cations, performance was better than that of mordenites and silicalites. Sorption of HCN in silicalite was observed in straight and zigzag channels, and mainly attributable to hydrogen bonding among HCN molecules. The increase in sodium cations however decreases the capacity of silicalite, zeolite beta and mordenite slightly. The sorption of MEK in mordenite was mainly observed in an 12- and 8-member ring channel. It was found that an increase in sodium cations did not increase the sorption capacity of mordenite significantly as most of the cations in mordenite were located in an 8-member ring channel where MEK molecules can not be accommodated properly due to steric effects. However, the sorption of MEK in zeolite beta seemed to be influenced by the presence of sodium cations as most of the cations are at the intersection of two 12 member rings which provide sufficient space to orient MEK molecules at the intersection and maximize electrostatic interactions. The sorption of MEK in silicalite exhibited similar trends as in the case of mordenite, as all cations were at the intersection of straight and zigzag channels . Finally, in the last Section of the Thesis, a comparative assessment was made of all three approaches in terms of their significance in applications and the ease in applying them.