Adsorption is a ubiquitous phenomenon that plays key roles in numerous applications including molecule separation, energy storage, catalysis, and lubrications. Since adsorption is sensitive to molecular details of adsorbate molecule and adsorbent materials, it is often difficult to describe theoretically. Molecular modeling capable of resolving physical processes at atomistic scales is an effective method for studying adsorption. In this dissertation, the adsorption of small molecules in three emerging materials systems: porous liquids, room-temperature ionic liquids, and atomically sharp electrodes immersed in aqueous electrolytes, are investigated to understand the physics of adsorption as well as to help design and optimize these materials systems. Thermodynamics and kinetics of gas storage in the recently synthesized porous liquids (crown-ether-substituted cage molecules dispersed in an organic solvent) were studied. Gas molecules were found to store differently in cage molecules with gas storage capacity per cage in the following order: CO2>CH4>N2. The cage molecules show selectivity of CO2 over CH4/N2 and demonstrate capability in gas separation. These studies suggest that porous liquids can be useful for CO2 capture from power plants and CH4 separation from shale gas. The effect of adsorbed water on the three-dimensional structure of ionic liquids [BMIM][Tf2N] near mica surfaces was investigated. It was shown that water, as a dielectric solvent and a molecular liquid, can alter layering and ordering of ions near mica surfaces. A three-way coupling between the self-organization of ions, the adsorption of interfacial water, and the electrification of the solid surfaces was suggested to govern the structure of ionic liquid near solid surfaces. The effects of electrode charge and surface curvature on adsorption of N2 molecules near electrodes immersed in water were studied. N2 molecules are enriched near neutral electrodes. Their enrichment is enhanced as the electrode becomes moderately charged but is reduced when the electrode becomes highly charged. Near highly charged electrodes, the amount of N2 molecules available for electrochemical reduction is an order of magnitude higher near spherical electrodes with radius ~1nm than near planar electrodes. The underlying molecular mechanisms are elucidated and their implications for development of electrodes for electrochemical reduction of N2 are discussed. / Doctor of Philosophy / Adsorption is a ubiquitous phenomenon that plays key roles in numerous applications including molecule separation, energy storage, catalysis, and lubrications. Since adsorption is sensitive to molecular details of adsorbate molecule and adsorbent materials, it is often difficult to describe theoretically. Molecular modeling capable of resolving physical processes at atomistic scales is an effective method for studying adsorption. In this dissertation, the adsorption of small molecules in three emerging materials systems: porous liquids, room-temperature ionic liquids, and atomically sharp electrodes immersed in aqueous electrolytes, are investigated to understand the physics of adsorption as well as to help design and optimize these materials systems. Thermodynamics and kinetics of gas storage in the recently synthesized porous liquids (crown-ether-substituted cage molecules dispersed in an organic solvent) were studied. Gas molecules were found to store differently in cage molecules with gas storage capacity per cage in the following order: CO2>CH4>N2. The cage molecules show selectivity of CO2 over CH4/N2 and demonstrate capability in gas separation. These studies suggest that porous liquids can be useful for CO2 capture from power plants and CH4 separation from shale gas. The effect of adsorbed water on the three-dimensional structure of ionic liquids [BMIM][Tf2N] near mica surfaces was investigated. It was shown that water, as a dielectric solvent and a molecular liquid, can alter layering and ordering of ions near mica surfaces. vi A three-way coupling between the self-organization of ions, the adsorption of interfacial water, and the electrification of the solid surfaces was suggested to govern the structure of ionic liquid near solid surfaces. The effects of electrode charge and surface curvature on adsorption of N2 molecules near electrodes immersed in water were studied. N2 molecules are enriched near neutral electrodes. Their enrichment is enhanced as the electrode becomes moderately charged but is reduced when the electrode becomes highly charged. Near highly charged electrodes, the amount of N2 molecules available for electrochemical reduction is an order of magnitude higher near spherical electrodes with radius ~1nm than near planar electrodes. The underlying molecular mechanisms are elucidated and their implications for development of electrodes for electrochemical reduction of N2 are discussed.

Aggregation of proteins into amyloid deposits is a common feature among dozens of diseases. Two such diseases that feature amyloid deposits are Alzheimer's disease (AD) and type 2 diabetes (T2D). AD toxicity has been associated with the aggregation and accumulation of the amyloid β-peptide (Aβ); Aβ exerts its toxic effects through interactions with neuronal cell membranes. A characteristic feature of T2D is the deposition of the islet amyloid polypeptide (IAPP) in the pancreatic islets of Langerhans. It is currently unknown if IAPP aggregation is a cause or consequence of T2D, but it does lead to β-cell dysfunction and death, exacerbating the effects of diabetes. Characterizing the fundamental interactions between both Aβ and IAPP with lipid membranes and in solution will give greater insight into mechanisms of toxicity exhibited by amyloid proteins. In this work, molecular dynamics (MD) simulations were used to study the secondary, tertiary, and quatnary structure of Aβ and IAPP, in addition to peptide-membrane interactions and membrane perturbation as independently caused by both peptides. Studies were conducted to address the following questions: (1) what influence do solution conditions and oxidation state have on monomeric Aβ] (2) how and in what way does monomeric Aβ interact with model lipid membranes and what role does sequence play on these peptide-membrane interactions; (3) can MD simulations be utilized to understand Aβ tetramer formation, rearrangement, and tetramer-membrane interactions; (4) how does IAP interact with model membranes and how does that vary from non-toxic (rat) IAPP peptide-membrane interactions. These studies led to conclusions that showed variance in lipid affinity and degree of perturbation as based on peptide sequence, in addition to insight into the type of perturbation caused to membranes by these amyloid peptides. Understanding the differences in peptide-membrane interactions of amyloidogenic and non-amyloidogenic (rat) peptides gave insight into the overall mechanism of amyloidogenicity, leading to the detection of specific amino acids essential in peptide-membrane perturbation. These residues can then be targeted for novel therapeutic design to attenuate the perturbation and potential cell death as caused by these peptides. / Ph. D.

Transport phenomena in heterogeneous media play a crucial role in numerous engineering applications such as hydrocarbon recovery from shales and material processing. Understanding and predicting these phenomena is critical for the success of these applications. In this dissertation, nanoscale transport phenomena in porous media are studied through physics-based simulations, and the effective solution of forward and inverse transport phenomena problems in heterogeneous media is tackled using data-driven, deep learning approaches. For nanoscale transport in porous media, the storage and recovery of gas from ultra-tight shale formations are investigated at the single-pore scale using molecular dynamics simulations. In the single-component gas recovery, a super-diffusive scaling law was found for the gas production due to the strong gas adsorption-desorption effects. For binary gas (methane/ethane) mixtures, surface adsorption contributes greatly to the storage of both gas in nanopores, with ethane enriched compared to methane. Ethane is produced from nanopores as effectively as the lighter methane despite its slower self-diffusion than the methane, and this phenomenon is traced to the strong couplings between the transport of the two species in the nanopore. The dying of solvent-loaded nanoporous filtration cakes by a purge gas flowing through them is next studied. The novelty and challenge of this problem lie in the fact that the drainage and evaporation can occur simultaneously. Using pore-network modeling, three distinct drying stages are identified. While drainage contributes less and less as drying proceeds through the first two stages, it can still contribute considerably to the net drying rate because of the strong coupling between the drainage and evaporation processes in the filtration cake. For the solution of transport phenomena problems using deep learning, first, convolutional neural networks with various architectures are trained to predict the effective diffusivity of two-dimensional (2D) porous media with complex and realistic structures from their images. Next, the inverse problem of reconstructing the structure of 2D heterogeneous composites featuring high-conductivity, circular fillers from the composites' temperature field is studied. This problem is challenging because of the high dimensionality of the temperature and conductivity fields. A deep-learning model based on convolutional neural networks with a U-shape architecture and the encoding-decoding processes is developed. The trained model can predict the distribution of fillers with good accuracy even when coarse-grained temperature data (less than 1% of the full data) are used as an input. Incorporating the temperature measurements in regions where the deep learning model has low prediction confidence can improve the model's prediction accuracy. / Doctor of Philosophy / Multiphysics transport phenomena inside structures with non-uniform pores or properties are common in engineering applications, e.g., gas recovery from shale reservoirs and drying of porous materials. Research on these transport phenomena can help improve related applications. In this dissertation, multiphysics transport in several types of structures is studied using physics-based simulations and data-driven deep learning models. In physics-based simulations, the multicomponent and multiphase transport phenomena in porous media are solved at the pore scale. The recovery of methane and methane-ethane mixtures from nanopores is studied using simulations to track motions and interactions of methane and ethane molecules inside the nanopores. The strong gas-pore wall interactions lead to significant adsorption of gas near the pore wall and contribute greatly to the gas storage in these pores. Because of strong gas adsorption and couplings between the transport of different gas species, several interesting and practically important observations have been found during the gas recovery process. For example, lighter methane and heavier ethane are recovered at similar rates. Pore-scale modeling are applied to study the drying of nanoporous filtration cakes, during which drainage and evaporation can occur concurrently. The drying is found to proceed in three distinct stages and the drainage-evaporation coupling greatly affects the drying rate. In deep learning modeling, convolutional neural networks are trained to predict the diffusivity of two-dimensional porous media by taking the image of their structures as input. The model can predict the diffusivity of the porous media accurately with computational cost orders of magnitude lower than physics-based simulations. A deep learning model is also developed to reconstruct the structure of fillers inside a two-dimensional matrix from its temperature field. The trained model can predict the structure of fillers accurately using full-scale and coarse-grained temperature input data. The predictions of the deep learning model can be improved by adding additional true temperature data in regions where the model has low prediction confidence.

Kinases are involved in a multitude of signaling pathways, such as cellular growth, proliferation, and apoptosis, and have been discovered to be important in numerous diseases including cancer, Alzheimer's disease, cardiovascular health, rheumatoid arthritis, and fibrosis. Due to the involvement in a wide variety of disease types, kinases have been studied for exploitation and use as targets for therapeutics. There are many limitations with developing kinase target therapeutics due to the high similarity of kinase active site composition, making the utilization of new techniques to determine kinase exploitability for therapeutic design with high specificity essential for the advancement of novel drug strategies. In silico approaches have become increasingly prevalent for providing useful insight into protein structure-function relationships, offering new information to researchers about drug discovery strategies. This work utilizes streamlined computational techniques on an atomistic level to aid in the identification of orthologue and isoform exploitability, identifying new features to be utilized for future inhibitor design. By exploring two separate kinases and kinase targeting domains, we found that orthologues and isoforms contain distinct features, likely responsible for their biological roles, which can be utilized and exploited for selective drug development. In this work, we identified new exploitable features between kinase orthologues for treatment in Human African Trypanosomiasis and structural morphology differences between two kinase isoforms that can potentially be exploited for cancer therapeutic design. / Master of Science in Life Sciences / Numerous diseases such as cancer, Alzheimer's disease, cardiovascular disease, rheumatoid arthritis, and fibrosis have been attributed to different cell growth and survival pathways. Many of these pathways are controlled by a class of enzymes called kinases. Kinases are involved in almost every metabolic pathway in human cells and can act as molecular switches to turn on and off disease progression. Due to the involvement of these kinases' in a wide variety of disease types, kinases have been continually studied for the development of new drugs. Developing effective drugs for kinases requires an extensive understanding of the structural characteristics due to the high structural similarity across all kinases. In silico, or computational, techniques are useful strategies for drug development practices, offering new information into protein structure-function relationships, which in turn can be utilized in drug discovery advancements. Utilizing computational methods to explore structural features can help identify specific protein structural features, thus providing new strategies for protein specific inhibitor design. In this work, we identified new exploitable features between kinase orthologues for treatment in Human African Trypanosomiasis and structural morphology differences between two kinase isoforms that can potentially be exploited for cancer therapeutic design.

The selection of the forcefield is a crucial issue in any MD related work and there is no clear indication as to which of the many available forcefields is the best for protein analysis. Many recent literature surveys indicate that MD work may be hindered by two limitations, namely conformational sampling and forcefields used (inaccuracies in the potential energy function may bias the simulation toward incorrect conformations). However, the advances in computing infrastructures, theoretical and computing aspects of MD have paved the way to carry out a sampling on a sufficiently longtime scale, putting a need for the accuracies in the forcefield. Because there are established differences in MD results when using forcefields, we have sought to ask how we could assess common mobility segments from a protein by analysis of trajectories using three forcefields in a similar environment. This is important because, disparate fluctuations appear to be more at flexible regions compared to stiff regions; in particular, flexible regions are more relevant to functional activities of the protein molecule. Therefore, we have tried to assess the similarity in the dynamics using three well-known forcefields ENCAD, CHARMM27 and AMBERFF99SB for 61 monomeric proteins and identify the properties of dynamic residues, which may be important for function. The comparison of popular forcefields with different parameterization philosophy may give hints to improve some of the currently existing agnostics in forcefields and characterization of mobile regions based on dynamics of proteins with diverse folds. These may also give some signature on the proteins at the level of dynamics in relation to function, which can be used in protein engineering studies. Nanosecond level MD simulation(30ns) on 61 monomeric proteins were carried out using CHARMM and AMBER forcefields and the trajectories with ENCAD forcefield obtained from Dynameomics database. The trajectories were first analyzed to check whether structural and dynamic properties from the three forcefields similar choosing few parameters in each case. The gross dynamic properties calculated (root mean square deviation (RMSD), TM-score derived RMSD, radius of gyration and accessible surface area) indicated similarity in many proteins. Flexibility index analysis on 17 proteins, which showed a notable difference in the flexibility, indicated that tertiary interactions (fraction of nonnative stable hydrogen bonds and salt bridges) might be responsible for the difference in the flexibility index. The normalized subspace overlap and shape overlap score taken based on the covariance matrices derived from trajectories indicated that majority of the proteins show a range between 0.3-0.5 indicating that the first principal components from these proteins in different combinations may not match well. These results indicate that although dynamic properties in general are similar in many proteins. However, flexibility index and normalized subspace overlap score indicate that subspaces on the first principal component in many proteins may not match completely. The number of proteins showing a better correlation is higher in CHARMM-AMBER combinations than the other two. The structural features from trajectories have been computed in terms of fraction of secondary structure, hydrogen bonds, salt bridges and native contacts. Although secondary structures and native contacts are well preserved during the simulations, the tertiary interactions (hydrogen bonds) are lost in many proteins and may be responsible for the difference in the some of properties among forcefields. Comparison of simulation results to experimental structures in terms of Root mean square fluctuations, Accessible surface area and radius of gyration indicates that the simulations results are on par with the ones derived from experimental structures. We have tried to assess the flexibility in the proteins using normalized Root mean square fluctuations (nRMSF), which for a residue is the ratio of RMSF from simulation to that of crystal structure. We have selected a threshold for this nRMSF to indicate the mobile regions in a protein based on secondary structure analysis. Based on the threshold of nRMSF and conformational properties (deviation in the dihedral angles), we have classified the residue and evaluated the properties of rigid hinge residues and corresponding mobile residues in terms of residue propensity, secondary structure preference and accessible surface area ranges. Since the rigid dynamic residues represent the inherent mobility, they might be important for function. Therefore, we have tried to assess the functional relevance considering the dynamic mobile residues from each protein from each forcefield simulation with the residues important for the function (taken from literature and databases). It is observed that some residues found to be mobile from the simulation are found to match with the experimental ones, although in many cases the number of these mobile residues is higher compared to the experimental ones. In summary, an analysis of protein simulation trajectories using three forcefields on a set of monomeric protein has shown that the gross structural properties and secondary structures from many proteins remain similar, but there are differences as may be seen from flexibility index. However correlation in parameters from CHARMM and AMBER force field is better compared to other two combinations. The differences seen in some of structural properties may arise mainly due to the loss of few tertiary interactions as indicated by the fraction of native hydrogen bonds and salt bridges. Based on the nRMSF, mobile segments obtained from the simulations were identified, and some of the mobile segments are found to match the functionally important residues from the experimental ones. Our work indicates that there are still some differences in the properties from the simulations, which indicates that care must be exercised when choosing a forcefield, especially assessing the functionally relevant residues from the simulations.

Computational methods such as linear free energy relationships (LFERs) offer a useful high-throughput solution to quickly evaluate drug developability, e.g. membrane permeability, organic solvent/water partition coefficients, and solubility. LFERs typically assume the contribution of structural components/functional groups to the overall properties of a given molecule to be constant and independent. This dissertation describes a series of studies in which linear free energy relationships were developed to predict solvation of small organic molecules in lipid formulations, specifically, triglyceride containing solvents and phospholipid-based liposomes. The formation of intermolecular HBs in triglyceride solvents (homogenous with H-bond accepting ability) and intramolecular HBs within the bilayer barrier domain (hydrocarbon-like) proved to be the major factors to consider in developing LFERs to account for the increased oil/water partition coefficients and enhanced bilayer permeability of small organic molecules. The triglyceride solvent/water partition coefficients of a series of model compounds varying in polarity and H-bond donating/accepting capability were used to establish a correlation between the solvent descriptors and the ester concentration in these solvents using the Abraham LFER approach. The LFER analyses showed that the descriptors representing the polarizability and H-bond basicity of the solvents vary systematically with the ester concentration. A fragment-based LFER to predict membrane permeability or 1,9- decadiene/water partition coefficients of small organic molecules including small peptides was systematically constructed using a total of 47 compounds. Significant nonadditivity was observed in peptides in that the contribution of the peptide backbone amide to the apparent transfer free energy from water into the bilayer barrier domain is considerably smaller than that of a “well-isolated” amide and greatly affected by adjacent polar substituents on the C-termini. In order to explain the phenomenon of nonadditivity, the formation of intramolecular HBs and inductive effects of neighboring polar groups on backbone amide, were investigated using FTIR and MD simulations. Both spectroscopic and computational results provided supportive evidence for the hypothesis that the formation of intramolecular HBs in peptides is the main reason for the observed nonadditivity of Δ(ΔG°)-CONH-. The MD simulation results showed that the inductive effect of neighboring groups is not as important as the effect of intramolecular HBs.

Os príons são proteínas que causam um grupo de doenças neurodegenerativas invariavelmente fatais, sendo uma das mais conhecidas a encefalopatia espongiforme bovina (ou doença da vaca louca). A proteína príon celular (PrPc), rica em estrutura α-helicoidal, sofre uma mudança na sua estrutura secundária produzindo a proteína patológica (PrPSc; o príon) na qual prevalecem folhas-β. Devido a falta de dados de estruturais de alta resolução dos príons, simulações de DM podem ser particularmente úteis para estudar o redobramento de PrP. Estudos experimentais e computacionais, descritos na literatura, indicam que a utilização de pH ácido é capaz de criar alguma instabilidade estrutural, produzindo um ganho de estrutura-β na região N-terminal antes desestruturada. Este trabalho se propõe a investigar computacionalmente as mudanças estruturais na proteína príon celular do hamster Sírio induzidas por alteração de pH. Para isso, foi avaliada a influência do uso de diferentes campos de força (GROMOS, AMBER e OPLS), diferentes estados de protonação dos resíduos de histidina, diferentes condições iniciais e diferentes métodos de cálculo de interações eletrostáticas de longo alcance (GRF e SPME). A partir da evolução temporal das estruturas secundárias, foi observada uma forte dependência dos resultados com o uso de diferentes parâmetros de simulação. De fato, a tendência de pH descrita na literatura não foi claramente observada neste trabalho. Isso pode estar associado com a necessidade de se investir mais em múltiplas simulações de dinâmica molecular para quantificar com maior precisão o comportamento estrutural dos fragmentos protéicos em cada pH de estudo. / Prions are proteins that cause a group of invariably fatal neurodegenerative diseases, one of the most known being bovine spongiform encephalopathy (or mad cow disease). The cellular prion protein (PrPc), rich in α-helical structure, undergoes a change in its secondary structure producing the pathological protein (PrPSc, the prion) in which β-sheet structure prevails. Because of the lack of high-resolution prion structural data, MD simulations can be particularly useful to study PrP misfolding. Experimental and computational studies, described in literature, indicate that the use of low pH is capable to create some structural instability, producing a gain of β-structure content in the otherwise unstructured N-terminal region. This work aims to investigate computationally structural changes in the cellular prion protein of Syrian hamster induced by pH change. For this, we evaluated the influence of different force fields (GROMOS, AMBER and OPLS), different protonation states of histidine residues, different initial conditions and different methods for calculating long-range electrostatic interactions (GRF and SPME). From the time evolution of the secondary structures, we observed a strong dependence on the simulation parameters. In fact, the pH tendency described in literature was not clearly observed in this work. It may be associated with the need to invest more in multiple molecular dynamics simulations to quantify more accurately the structural behavior of the protein fragments in each pH study.

One of the most promising routes to create advanced materials is self-assembly. Self-assembly refers to the self-organisation of building blocks to form ordered structures. As the properties of the self-assembled materials will inherit the properties of the basic building blocks, it is then possible to engineer the properties of the materials by tailoring the properties of the building blocks. In order to create mesoscale materials, the self-assembly of molecular building blocks of different sizes and interactions is important. Mesoscopic materials can be obtained by using larger building blocks such as nano and colloidal particles. Colloidal particles are particularly attractive as building blocks because it is possible to design interparticle interactions by controlling both the chemistry of the particles' surface and the properties of the solvent in which the particles are immersed. The self-assembly of spherical colloidal particles has been widely reported in the literature. However, advances in experimental techniques to produce particles with different shapes and sizes have opened new opportunities to create more complex structures that cannot be formed using spherical particles. Indeed, the particles' shape and effective interactions between them dictate the spatial arrangement and micro-structure of the system, which can be engineered to produce functional materials for a wide range of applications. The driving forces determining the self-assembly of colloidal particles can be modified by the use of external influences such as geometrical confinement and electromagnetic forces. Geometrical confinement, for example, has been used to design quasi two-dimensional materials such as multi-layered structures of spheres, dimers, rods, spherical caps, and monolayers of platelets with various geometries and symmetries. In this dissertation, we present three computer simulations studies using Monte Carlo and Molecular Dynamics simulations determining the self-assembly of monolayer colloidal platelets with different shapes confined in two dimensions. These particles have been selected due to recent experiments in colloidal particles with similar shapes. All the particles' models are represented by planar polygons, and three different effects affecting their self-assembly have been analysed: (a) the curvature of the particles' vertices; (b) the curvature of the particles' edges; and finally (c) the addition of functional groups on the particles' surface. These studies aim to demonstrate that the subtle changes on the particle's shape can be used to engineer complex patterns for the fabrication of advanced materials. Monte Carlo simulations are performed to study the self-assembly of colloidal platelets with rounded corners with 4, 5, and 6-fold symmetries. Square platelets provide a rich phase behaviour that ranges between disorder-order and order-order phase transitions. Suprisingly, the disk-like shape of pentagons and hexagons prevents the total crystallisation of these systems, even at a high pressure state. A hysteresis gap is observed by the analysis of compression and expansion runs for the case of square platelets and the thermodynamic method known as direct coexistence method is used to be accurately determined the point of the order-order transition. Further, unexpected results are obtained by performing Molecular Dynamics simulations in systems with platelets with 3, 4, 5, and 6-fold symmetries when all the sides of each polygon are curved. Macroscopic chiral symmetry breaking is observed for platelets with 4 and 6-fold symmetries, and for the first time a rule is promoted to explain when these chiral structures can be formed driven only by packing effects. This unique rule is verified also for platelets with the same curved sides as previously when functional chains tethered to either vertices or sides. Indeed, square platelets with curved sides confined in two dimensions can form chiral structures at medium densities when flexible chains tethered to either vertices or sides. Triangular platelets with curved sides can form chiral structures only when the chains are tethered to the corners, since the chains experience an one-hand rotation to sterically protect one side. When the chains are symmetrically tethered to the sides, local chiral symmetry breaking is observed as both left-hand and right-hand sides on each vertex are sterically protected allowing the same probability for rotation either in clockwise or anticlockwise direction.

Made available in DSpace on 2014-06-11T19:31:03Z (GMT). No. of bitstreams: 0 Previous issue date: 2012-06-07Bitstream added on 2014-06-13T19:40:48Z : No. of bitstreams: 1 lopesfilho_fc_dr_sjrp_parcial.pdf: 183424 bytes, checksum: 8601f6f72a7635a3c9ada79092a5873d (MD5) Bitstreams deleted on 2015-06-25T13:01:06Z: lopesfilho_fc_dr_sjrp_parcial.pdf,. Added 1 bitstream(s) on 2015-06-25T13:03:24Z : No. of bitstreams: 1 000694954_20160706.pdf: 183130 bytes, checksum: 1526b9f9e1347a4fb71fe218102cf0ba (MD5) Bitstreams deleted on 2016-07-25T13:17:36Z: 000694954_20160706.pdf,. Added 1 bitstream(s) on 2016-07-25T13:18:45Z : No. of bitstreams: 1 000694954.pdf: 1071220 bytes, checksum: ead8820e5de7c1e29fdd2ec0459005b1 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Peptídeos antimicrobianos são moléculas biologicamente ativas que, geralmente, tem as membranas fosfolipídicas como alvo primário. Resultados de diferentes técnicas experimentais têm sugerido que esses peptídeos permeabilizam as membranas pela formação de poros. Parte dos peptídeos caracterizados apresentam especificidade de disrupção para membranas de bactérias, em detrimento das membranas dos hospedeiros. Essa característica tem atraído a atenção da comunidade científica internacional, porque indica que estas moléculas podem ser modelos para o desenvolvimento de novos antibióticos, portanto o entendimento do mecanismo de ação, ou seja, do mecanismo de formação de poro, tem extrema importância. Simulações por Dinâmica Molecular foram produzidas para investigarmos o impacto que peptídeos antimicrobianos da família Mastoparano tem sobre membranas lipídicas modelo. Dois cenários foram explorados: (i) de baixa concentração peptídeo/lipídeo, P/L=1/128, que consistia de simulações fine-grained das interações de um peptídeo com uma bicamada pura de 128 lipídeos aniônicos (POPG) ou zwiteriônicos (POPC); (ii) de alta concentração, P/L=1/21, que abordava as interações de seis peptídeos com uma bicamada mista de 128 lipídeos POPC/POPG (1/1) usando uma modelagem coarse-grained. Tomando o peptídeo MP1 como caso paradigmático, verificamos que em baixo P/L é possível sugerir que sua característica seletiva surge da capacidade de coordenar e perturbar maior número de lipídeos em membrana aniônica comparada à neutra. Essa capacidade fica acentuada nas simulações com membrana mista, onde a atração dos lipídeos aniônicos pelos peptídeos catiônicos guiou a separação local e a formação de domínios de lipídeos aniônicos, o que facilitou o afinamento local da membrana e a formação de poro transmembrânico. Esses achados ajudam a explicar como peptídeos / Antimicrobial peptides are biologically active molecules that, usually, have the phospholipid membranes as a primary target. Results from different experimental techniques have suggested these peptides permeabilize membranes by the pore formation. Part of the characterized peptides have specificity of disruption for bacterial membranes, instead of host membrane. This feature has attracted the attention of the international scientific community, because it indicates that these molecules can be models for the development of novel antibiotics, so understanding the mechanism of action, ie, the mechanism of pore formation, is extremely important. Molecular dynamics simulations were performed to investigate the impact of antimicrobial peptides from the Mastoparano family have on model lipid membranes. Two scenarios were explored: (i) of low peptide/lipid concentration, P/L=1/128, which consisted of fine-grained simulations of the interactions of a peptide with a pure bilayer of 128 anionic (POPG) or zwitterionic (POPC) lipids; (ii) of high concentration, P/L=1/21, which addressed the interactions of six peptides with a mixed bilayer of 128 POPC/POPG (1/1) lipids, using a coarse-grained modeling. Taking the MP1 peptide as a paradigmatic case, we found that in low P/L is possible to suggest that its selective feature arises of its ability to coordinate and disturb large number of lipids in the anionic membrane compared to neutral one. This ability is accentuated in simulations with mixed membrane, where the attraction of the anionic lipids by the cationic peptides led to the local segregation and formation of POPG lipid domains, which facilitated the local thinning of the membrane and the formation of transmembrane pore. These findings help to explain how short peptides, such as MP1, are able of forming pores in a membrane whose thickness is larger than the length of the peptide

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / Nesta dissertaÃÃo, fizemos um estudo das propriedades difusivas de um sistema de partÃculas clÃssicas carregadas em canais quasi-unidimensionais. Mais especificamente, no CapÃtulo 2, apresentamos uma revisÃo do problema da difusÃo e do movimento browniano. Mostramos que as abordagens de Einstein e de Langevin para o movimento browniano sÃo equivalentes no limite de tempos longos. Isto foi feito atravÃs do cÃlculo analÃtico do deslocamento quadrÃtico mÃdio (MSD) de um sistema unidimensional de N partÃculas nÃo--interagentes atravÃs da soluÃÃo da equaÃÃo de difusÃo. No CapÃtulo 3, introduzimos o mÃtodo de DinÃmica Molecular (DM), amplamente utilizado em simulaÃÃes computacionais de sistemas de N partÃculas clÃssicas. Apresentamos dois mÃtodos de integraÃÃo numÃrica das equaÃÃes de movimento: o algoritmo de Verlet e o algoritmo leapfrog. Abordamos brevemente o mÃtodo de DinÃmica Molecular de Langevin (DML), que inclui um termo de flutuaÃÃes tÃrmicas (forÃa estocÃstica), devido Ãs colisÃes das molÃculas do fluido com as partÃculas do sistema. Finalmente, apresentamos uma aproximaÃÃo do mÃtodo de DML chamada DinÃmica Browniana (DB). No CapÃtulo 4, estudamos as propriedades difusivas, atravÃs da anÃlise do deslocamento quadrÃtico mÃdio, de um sistema de partÃculas clÃssicas carregadas sujeitas à aÃÃo de um potencial de confinamento unidimensional, analisando a transiÃÃo do regime de difusÃo em linha (SFD) para o regime de difusÃo bidimensional (2D). Vimos como ocorre essa transiÃÃo em funÃÃo dos parÃmetros que regulam o potencial de confinamento. Discutimos a validade dos resultados numÃricos obtidos em relaÃÃo a resultados analÃticos teÃricos encontrados na literatura. Finalmente, no CapÃtulo 5, apresentamos um resumo dos resultados obtidos, bem como discutimos perspectivas e sugestÃes para futuros trabalhos.