Made available in DSpace on 2014-10-09T12:42:33Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:01:46Z (GMT). No. of bitstreams: 0 / Tese (Doutoramento) / IPEN/T / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Made available in DSpace on 2014-10-09T12:26:16Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:09:46Z (GMT). No. of bitstreams: 0 / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

abstract: The goal of this theoretical study of infrared spectra was to ascertain to what degree molecules may be identified from their IR spectra and which spectral regions are best suited for this purpose. The frequencies considered range from the lowest frequency molecular vibrations in the far-IR, terahertz region (below ~3 THz or 100 cm-1) up to the highest frequency vibrations (~120 THz or 4000 cm-1). An emphasis was placed on the IR spectra of chemical and biological threat molecules in the interest of detection and prevention. To calculate IR spectra, the technique of normal mode analysis was applied to organic molecules ranging in size from 8 to 11,352 atoms. The IR intensities of the vibrational modes were calculated in terms of the derivative of the molecular dipole moment with respect to each normal coordinate. Three sets of molecules were studied: the organophosphorus G- and V-type nerve agents and chemically related simulants (15 molecules ranging in size from 11 to 40 atoms); 21 other small molecules ranging in size from 8 to 24 atoms; and 13 proteins ranging in size from 304 to 11,352 atoms. Spectra for the first two sets of molecules were calculated using quantum chemistry software, the last two sets using force fields. The "middle" set used both methods, allowing for comparison between them and with experimental spectra from the NIST/EPA Gas-Phase Infrared Library. The calculated spectra of proteins, for which only force field calculations are practical, reproduced the experimentally observed amide I and II bands, but they were shifted by approximately +40 cm-1 relative to experiment. Considering the entire spectrum of protein vibrations, the most promising frequency range for differentiating between proteins was approximately 600-1300 cm-1 where water has low absorption and the proteins show some differences. / Dissertation/Thesis / Ph.D. Physics 2012

Made available in DSpace on 2014-10-09T12:42:33Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:01:46Z (GMT). No. of bitstreams: 0 / O desenvolvimento de sistemas de conversão de energia baseados na tecnologia de células a combustível tem demandado estudos de confiabilidade, uma vez que requisitos de durabilidade e custo passaram a ser fundamentais para a inserção desta tecnologia no mercado de energia. Neste trabalho foi proposta uma metodologia de análise de confiabilidade de células a combustível de membrana polimérica condutora de prótons (células a combustível do tipo PEM), tendo em vista a qualificação destes itens como protótipos de unidades comerciais. A metodologia proposta incluiu uma avaliação inicial qualitativa das possíveis falhas em células a combustível do tipo PEM, realizada pela aplicação de uma Análise de Modos de Falha e Efeitos, técnica conhecida pela sigla FMEA. Além disso, foi elaborado um plano de testes de vida para as unidades produzidas no IPEN CNEN/SP e foi efetuada a análise dos resultados usando-se métodos estatísticos para dados de confiabilidade. Assim, a parte experimental consistiu em produzir as células a combustível no laboratório e submetê-las aos testes de vida, nos quais foram simuladas condições reais de operação. Os dados amostrais foram analisados estatisticamente, gerando resultados importantes em relação às medidas de desempenho e durabilidade dos dispositivos em estudo. Por meio de uma análise não paramétrica, foi gerada uma estimativa da função de confiabilidade das células a combustível usando-se o estimador de Kaplan- Meier. Pela modelagem paramétrica, foi possível ajustar uma distribuição exponencial para o tempo de vida destes dispositivos, gerando uma estimativa de vida média de 1.094,58 horas, com intervalo de 95% de confiança de [533,03 horas; 2.836,13 horas]. Com relação ao desempenho, foi aplicada uma regressão linear simples aos dados de potencial elétrico ao longo do tempo, gerando um valor aproximado para a taxa de queda do potencial elétrico de 80 μV h-1. Cabe ressaltar que, a metodologia proposta neste estudo deverá ser integrada ao processo de desenvolvimento das células a combustível, para permitir o acompanhamento da melhoria de sua confiabilidade. / Tese (Doutoramento) / IPEN/T / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Made available in DSpace on 2014-10-09T12:26:16Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:09:46Z (GMT). No. of bitstreams: 0 / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

A caracterização das flutuações dos resíduos da proteína em torno do seu estado nativo é essencial para estudar mudanças conformacionais, interação proteína-proteína e interação proteína-ligante. Tal caracterização pode ser capturada pelo modelo de rede gaussiana (GNM). Este modelo tem sido modificado e novas propostas têm surgido nos últimos anos. Nesta Tese, apresentamos um estudo sobre como melhorar o GNM e exploramos o seu desempenho em predizer os fatores-B experimentais. Modelos de redes elásticas são construídos a partir das coordenadas experimentais dos levando em consideração pares de átomos de C? distantes entre si até um dado raio de corte Rc . Estes modelos descrevem as interações entre os atómos por molas com a mesma constante de força. Desenvolvemos um método baseado em simulações numéricas com um campo de forças simplificado para atribuir pesos a estas constantes de mola. Este método considera o tempo em que dois átomos de C? permanecem conectados na rede durante o desenovelamento parcial, estabelecendo assim uma forma de medir a intensidade de cada ligação. Examinamos dois diferentes campos de forças simplificados e exploramos o cálculo desses pesos a partir do desenovelamento das estruturas nativas. Nós comparamos o seu desempenho na predição dos fatores-B com outros modelos de rede elástica. Avaliamos tal desempenho utilizando o coeficiente de correlação entre os fatores-B preditos e experimentais. Mostramos como o nosso modelo pode descrever melhor os fatores-B / The characterization of the fluctuations in protein residues around its native state is essential to study conformational changes, protein binding interaction and protein-protein interaction. Such characterization can be captured by simple elastic network models as the Gaussian Network Model (GNM). This model has been modified and new proposals have emerged in recent years. In this Thesis we propose an extended version of GNM, namely wGNM. Elastic network models are built on the experimental C? coordinates,and they only take the pairs of C? atoms within a given cutoff distance Rc into account. These models describe the interactions by elastic springs with the same force constant to predicted the experimental B-factors, providing insights into the structure-function properties of proteins. We have developed a method based on numerical simulations with a simple coarse-grained force field, to attribute weights to these spring constants. This method considers the time that two C? atoms remain connected in the network during partial unfolding, establishing a means of measuring the strength of each link. We examined two different coarse-grained force fields and explored the computation of these weights by unfolding native structures. We compare the B-factors predicted by different elastic network models with the experimental ones employing the correlation coefficient between these two quantities. We show that wGNM performs better and consequently provides better evaluation of the B-factors

A large component of the State of Utah's transportation network involves the use of MSE walls, which have proven useful in infrastructure for their reduced costs and footprint compared to other alternatives. As effective as MSE walls have been in responding to demands in transportation, they also have inherent challenges. For the majority of MSE walls the structure is limited in observation as structural components are buried as part of the soils mass. This inability to observe at can lead to the development of complex failure mechanisms, which can be difficult to assess and anticipate. As society becomes increasingly reliant on the transportation networks for goods, services, and security, properly understanding the potential failure mechanisms of MSE walls also increases in importance. This thesis discusses the development of an inspection procedure, data collection, geotechnical asset management database, and an evaluation of gathered information to be used in a reliability analysis of MSE walls for the State of Utah. The findings suggest areas of improvement in the design, specifications, maintenance, and further investigation of MSE walls.

A novel method of analysis of macromolecules has been worked upon through this research. In an effort to understand the dynamics of macromolecules and to further our knowledge, pertaining specifically to the low frequency domain and also to elucidate certain important biological functions associated with it, a theoretical technique of chemical information based Normal Mode Analysis has been developed. These simulations render users with the ability to generate animations of modeshapes as well as key insight on the associated vibration frequencies. Harmonic analysis using atomistic details is performed taking into account appropriate values of masses of constituent atoms of a given macromolecule. In order to substantiate the applicability of such a technique, simple linear molecules were first worked upon. Subsequently, this technique has been applied to relatively more complex structures like amino acids, namely Cysteine. Consequently, this approach was extended to large macromolecules like Lactoferrin. Animations of modeshapes from simulations suggest a one to one correspondence with other computational techniques reported by other researchers. Computed β-factors are also in close agreement with the experimentally observed values of the same. Hence, as opposed to a simple Cα coarse grained model, our method with right masses and reasonable force fields yields not only the correct modeshapes but also provides us with useful information on wavenumbers that can be used to extract useful information about the frequency domain. Moreover, as opposed to conventional Molecular Dynamics’ simulations and Laser spectroscopy, the proposed methodology is significantly faster, cheaper and efficient.

No description available.

<p dir="ltr">Proteins and their interactions with each other, with nucleic acids, and with other molecules are foundational to all known forms of life. The three-dimensional structures of these interactions are an essential component of a comprehensive understanding of how they function. Molecular-biological hypothesis formulation and rational drug design are both often predicated on a particular structure model of the molecule or complex of interest. While experimental methods capable of determining atomic-detail structures of molecules and complexes exist, such as the popular X-ray crystallography and cryo-electron microscopy, these methods require both laborious sample preparation and expensive instruments with limited throughput. Computational methods of predicting complex structures are therefore desirable if they can enable cheap, high-throughput virtual screening of the space of biological hypotheses. Many common biomolecular contexts have largely been blind spots for predictive modeling of complex structures. In this direction, docking methods are proposed to address extreme conformational change, nonuniform environments, and distance-geometric priors. Flex-LZerD deforms a flexible protein using a novel fitting procedure based on iterated normal mode decomposition and was shown to construct accurate complex models even when an initial input subunit structure exhibits extreme conformational differences from its bound state. Mem-LZerD efficiently constrains the docking search space by augmenting the geometric hashing data structure at the core of the LZerD algorithm and enabled membrane protein complexes to be efficiently and accurately modeled. Finally, atomic distance-based approaches developed during modeling competitions and collaborations with wet lab biologists were shown to effectively integrate domain knowledge into complex modeling pipelines.</p>