Amyloidogenic diseases, such as Alzheimer's disease (AD) and Type II Diabetes (T2D), are characterized by the accumulation of amyloid aggregates. Despite having very different amino-acid sequences, the underlying amyloidogenic proteins form similar supramolecular fibril structures that are highly stable and resistant to physical and chemical denaturation. AD is characterized by two toxic lesions: extracellular amyloid β-peptide (Aβ) plaques and intracellular neurofibrillary tangles composed of microtubule-associated protein tau. Similarly, a feature of T2D is the deposition of islet amyloid polypeptide (IAPP) aggregates in and around the pancreas. The mechanisms by which Aβ, tau, and IAPP aggregate, and cause cell death is unknown; thus, gaining greater insight into the stabilizing forces and initial unfolding events is crucial to our understanding of these amyloidogenic diseases. This work uses molecular dynamics (MD) simulations to study the secondary, tertiary, and quaternary structure of Aβ, tau, and IAPP. Specifically, this work used the Drude polarizable force field (FF), which explicitly represents electronic polarization allowing charge distributions to change in response to perturbations in local electric fields. This model allows us to describe the role charge plays on protein folding and stability and how perturbations to the charge state drive pathology. Studies were conducted to address the following questions: 1) What are the stabilizing forces of fibril and oligomeric structures? 2) How do charge-altering mutations modulate the conformational ensemble and thermodynamic properties of Aβ? 3) How do charge-altering post-translational modifications of Aβ and tau modulate changes in the conformational ensembles? These studies establish that shifts in local microenvironments play a role in fibril and oligomer stability. Furthermore, these studies found that changes in protein sequence and charge are sufficient to disrupt and change the secondary and tertiary structure of these amyloidogenic proteins. Overall, this dissertation describes how charge modulates protein unfolding and characterizes the mechanism of those changes. In the long term, this work will help in the development of therapeutics that can target these changes to prevent protein aggregation that leads to cell death. / Doctor of Philosophy / Protein aggregation is the hallmark of many chronic diseases, such as Alzheimer's disease (AD) and Type II Diabetes (T2D). The formation of two toxic aggregates: amyloid β-peptide (Aβ) plaques and neurofibrillary tangles composed of microtubule-associated protein tau are some of the key characteristics of AD. In addition, the formation of islet amyloid polypeptide (IAPP) aggregates in the pancreas is thought to play a role in the development of T2D. The pathways by which the proteins Aβ, tau, and IAPP aggregate are unknown; thus, gaining a greater insight into the properties that may cause these diseases is necessary to develop treatments. By studying these proteins at the atomistic level, we can understand how small changes to these proteins alter how they misfold in a way that promotes toxicity. Herein, we used a computational technique called molecular dynamics (MD) simulations to gain new insights into how protein structure changes. We explored the dynamics of these proteins and investigated the role that charge plays in protein folding and described how charge modulates protein folding and characterized the mechanism of those changes. This work serves as a characterization of protein folding and sets the ground for future structural studies and drug development.

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The aim of this research is to investigate the extent to which Open Source Software (OSS) adoption behaviour can empirically be shown to be governed by a set of self-reported (driving and inhibiting) salient beliefs of key informants in a sample of organisations. Traditional IS adoption/usage theory, methodology and practice are drawn on. These are then augmented with theoretical constructs derived from IT governance and organisational diagnostics to propose an artefact that aids the understanding of organisational OSS adoption behaviour, stimulates debate and aids operational management interventions. For this research, a combination of quantitative methods (via Fisher's Exact Test) and complimentary qualitative method (via Content Analysis) were used using self-selection sampling techniques. In addition, a combination of data and methods were used to establish a set of mixed-methods results (or meta-inferences). From a dataset of 32 completed questionnaires in the pilot study, and 45 in the main study, a relatively parsimonious set of statistically significant driving and inhibiting factors were successfully established (ranging from 95% to 99.5% confidence levels) for a variety for organisational OSS adoption behaviours (i.e. by year, by software category and by stage of adoption). In addition, in terms of mixed-methods, combined quantitative and qualitative data yielded a number of factors limited to a relatively small number of organisational OSS adoption behaviour. The findings of this research are that a relatively small set of driving and inhibiting salient beliefs (e.g. Security, Perpetuity, Unsustainable Business Model, Second Best Perception, Colleagues in IT Dept., Ease of Implementation and Organisation is an Active User) have proven very accurate in predicting certain organisational OSS adoption behaviour (e.g. self-reported Intention to Adopt OSS in 2014) via Binomial Logistic Regression Analysis.

This master thesis analyzes the question, how norm authority constitutes itself in the context of school education. It is based on firsthand data from several adio- and audiovisual recordings of classes in two German schools (trade school and high school). The project provides a description of the mechanism by which the teacher establishes himself as the norm-authority and how he is able to assert this authority towards the student, the norm subject. At the same time, the reaction of the norm subjects towards the intervention of the norm authority into their language production is analyzed. Theoretically, the essay is embedded in the social force field of a standard-variety ("soziales Kräftefeld einer Standardvarietät") by Ulrich Ammon and the language management theory. By following the steps of the language management process, the handling of the language norm is described and an analysis is made of how language norms are actively adopted into the learning process. The analysis and evaluation of the class recordings underline the dynamic character of language norms and show the procedural nature of norm formation and their adoption. Key Words: norm, variety, social force field, language management, macro and micro level, norm authority, norm subject

A capacidade de prever a estrutura nativa de uma proteína é um problema ainda sem solução. A predição da estrutura final ou nativa de uma proteína -, ou seja, partir da estrutura primária (sequência de aminoácidos linear) de um polipeptídeo tentar prever qual será a estrutura terciária (arranjo de hélices alfa, folhas beta e grampos) - tem sido um desafio para diversos pesquisadores desde o século passado. Atualmente existem diversos modelos que se propõem a executar essa tarefa, mas poucos que de fato partem de princípios físicos básicos para realizá-la. A grande maioria baseia-se em estruturas já conhecidas de proteínas com sequenciamento ou função similares para prever a estrutura terciária. Neste trabalho, no entanto, propõe-se o desenvolvimento de um campo de forças coarse-grained para aplicação em simulações de dinâmica molecular a fim de prever a estrutura de proteínas sem que seja necessária a comparação com estruturas já conhecidas. O fator de forma é um importante indicativo da estrutura de uma molécula em solução. Apesar de se tratar de uma grandeza que fornece informações de baixa resolução, ou seja, não fornece pormenores a respeito da posição dos átomos na estrutura da molécula, é uma estimativa inicial do espaço ocupado pela molécula e também da maneira com a qual ela interage com outras moléculas em solução. Isso é decorrente das operações matemáticas necessárias para que o fator de forma seja acessado a partir de dados de experimentos de espalhamento de raios X. Os resultados mostram que o método consegue prever uma estrutura condizente com os dados de espalhamento de raios X das estruturas cristalográficas e com os dados experimentais utilizados. / The prediction of the final structure of a protein (also called native structure) has been addressed by many research groups since the last century. This problem can be understood as how to predict the tertiary structure that a protein molecule assumes after the folding process from just the primary structure (the sequence of amino acids). Nowadays there are several models aiming at solving this problem, but very few of them are based on physical principles. Most of these models are template-based ones that search for similar amino acids sequences or analogous biological functions to predict the native structure. In the present work, however, we propose the development of a force field predict the form factor of proteins that does not entail the knowledge of any other model or template to do so. The form factor is an important aspect of the structure of a molecule in solution. Despite being a low-resolution method of analysis, in the sense that it does not provide details about the exact positions of the atoms inside the molecular structure (because of the mathematical operations needed to retrieve informations from the scattering data), it is an initial estimative of the volume occupied by this molecule and also a good initial path for uncovering how these molecules interact to each other in solution. The results show that the method presented here can predict a structure that agrees with the scattering data of the crystallographic structure and with available experimental data of x-ray scattering of proteins in solution.

Human sensorimotor control is remarkably adept at utilising contextual information to learn and recall systematic sensorimotor transformations. Here, we investigate the motor representations that underlie such learning, and examine how motor memories acquired based on different contextual information interact. Using a novel three-dimensional robotic manipulandum, the 3BOT, we examined the spatial transfer of learning across various movement directions in a 3D environment, while human subjects performed reaching movements under velocity-dependent force field. The obtained pattern of generalisation suggested that the representation of dynamic learning was most likely defined in a target-based, rather than an extrinsic, coordinate system. We further examined how motor memories interact when subjects adapt to force fields applied in orthogonal dimensions. We found that, unlike opposing fields, learning two spatially orthogonal force fields led to the formation of separate motor memories, which neither interfered with nor facilitated each other. Moreover, we demonstrated a novel, more general aspect of the spontaneous recovery phenomenon using a two-dimensional force field task: when subjects learned two orthogonal force fields consecutively, in the following phase of clamped error feedback, the expression of adaptation spontaneously rotated from the direction of the second force field, towards the direction of the first force field. Finally, we examined the interaction of sensorimotor memories formed based on separate contextual information. Subjects performed reciprocating reaching and object manipulation tasks under two alternating contexts (movement directions), while we manipulated the dynamics of the task in each context separately. The results suggested that separate motor memories were formed for the dynamics of the task in different contexts, and that these motor memories interacted by sharing error signals to enhance learning. Importantly, the extent of interaction was not fixed between the context-dependent motor memories, but adaptively changed according to the task dynamics to potentially improve overall performance. Together, our experimental and theoretical results add to the understanding of mechanisms that underlie sensorimotor learning, and the way these mechanisms interact under various tasks and different dynamics.

A capacidade de prever a estrutura nativa de uma proteína é um problema ainda sem solução. A predição da estrutura final ou nativa de uma proteína -, ou seja, partir da estrutura primária (sequência de aminoácidos linear) de um polipeptídeo tentar prever qual será a estrutura terciária (arranjo de hélices alfa, folhas beta e grampos) - tem sido um desafio para diversos pesquisadores desde o século passado. Atualmente existem diversos modelos que se propõem a executar essa tarefa, mas poucos que de fato partem de princípios físicos básicos para realizá-la. A grande maioria baseia-se em estruturas já conhecidas de proteínas com sequenciamento ou função similares para prever a estrutura terciária. Neste trabalho, no entanto, propõe-se o desenvolvimento de um campo de forças coarse-grained para aplicação em simulações de dinâmica molecular a fim de prever a estrutura de proteínas sem que seja necessária a comparação com estruturas já conhecidas. O fator de forma é um importante indicativo da estrutura de uma molécula em solução. Apesar de se tratar de uma grandeza que fornece informações de baixa resolução, ou seja, não fornece pormenores a respeito da posição dos átomos na estrutura da molécula, é uma estimativa inicial do espaço ocupado pela molécula e também da maneira com a qual ela interage com outras moléculas em solução. Isso é decorrente das operações matemáticas necessárias para que o fator de forma seja acessado a partir de dados de experimentos de espalhamento de raios X. Os resultados mostram que o método consegue prever uma estrutura condizente com os dados de espalhamento de raios X das estruturas cristalográficas e com os dados experimentais utilizados. / The prediction of the final structure of a protein (also called native structure) has been addressed by many research groups since the last century. This problem can be understood as how to predict the tertiary structure that a protein molecule assumes after the folding process from just the primary structure (the sequence of amino acids). Nowadays there are several models aiming at solving this problem, but very few of them are based on physical principles. Most of these models are template-based ones that search for similar amino acids sequences or analogous biological functions to predict the native structure. In the present work, however, we propose the development of a force field predict the form factor of proteins that does not entail the knowledge of any other model or template to do so. The form factor is an important aspect of the structure of a molecule in solution. Despite being a low-resolution method of analysis, in the sense that it does not provide details about the exact positions of the atoms inside the molecular structure (because of the mathematical operations needed to retrieve informations from the scattering data), it is an initial estimative of the volume occupied by this molecule and also a good initial path for uncovering how these molecules interact to each other in solution. The results show that the method presented here can predict a structure that agrees with the scattering data of the crystallographic structure and with available experimental data of x-ray scattering of proteins in solution.

<p>Human Immunodeficiency Virus (HIV) is the causative agent of the pandemic disease Acquired Immune Deficiency Syndrome (AIDS). HIV acts to disrupt the immune system which makes the body susceptible to opportunistic infections. Untreated, AIDS is generally fatal. Twenty years of research by countless scientists around the world has led to the discovery and exploitation of several targets in the replication cycle of HIV. Many lives have been saved, prolonged and improved as a result of this massive effort. One particularly successful target has been the inhibition of HIV protease. In combination with the inhibition of HIV reverse transcriptase, protease inhibitors have helped to reduce viral loads and partially restore the immune system. Unfortunately, viral mutations leading to drug resistance and harmful side-effects of the current medicines have identified the need for new drugs to combat HIV.</p><p>This study presents computational efforts to understand the interaction of inhibitors to HIV protease. The first part of this study has used molecular modelling and Comparative Molecular Field Analysis (CoMFA) to help explain the structure-active relationship of a novel series of protease inhibitors. The inhibitors are sulfamide derivatives structurally similar to the cyclic urea candidate drug mozenavir (DMP-450). The central ring of the sulfamides twists to adopt a nonsymmetrical binding mode distinct from that of the cyclic ureas. The energetics of this twist has been studied with <i>ab initio</i> calculations to develop improved empirical force field parameters for use in molecular modelling.</p><p>The second part of this study has focused on an analysis of the association and dissociation kinetics of a broad collection of HIV protease inhibitors. Quantitative models have been derived using CoMFA which relate the dissociation rate back to the chemical structures. Efforts have also been made to improve the models by systematically varying the parameters used to generate them.</p>

Human Immunodeficiency Virus (HIV) is the causative agent of the pandemic disease Acquired Immune Deficiency Syndrome (AIDS). HIV acts to disrupt the immune system which makes the body susceptible to opportunistic infections. Untreated, AIDS is generally fatal. Twenty years of research by countless scientists around the world has led to the discovery and exploitation of several targets in the replication cycle of HIV. Many lives have been saved, prolonged and improved as a result of this massive effort. One particularly successful target has been the inhibition of HIV protease. In combination with the inhibition of HIV reverse transcriptase, protease inhibitors have helped to reduce viral loads and partially restore the immune system. Unfortunately, viral mutations leading to drug resistance and harmful side-effects of the current medicines have identified the need for new drugs to combat HIV. This study presents computational efforts to understand the interaction of inhibitors to HIV protease. The first part of this study has used molecular modelling and Comparative Molecular Field Analysis (CoMFA) to help explain the structure-active relationship of a novel series of protease inhibitors. The inhibitors are sulfamide derivatives structurally similar to the cyclic urea candidate drug mozenavir (DMP-450). The central ring of the sulfamides twists to adopt a nonsymmetrical binding mode distinct from that of the cyclic ureas. The energetics of this twist has been studied with ab initio calculations to develop improved empirical force field parameters for use in molecular modelling. The second part of this study has focused on an analysis of the association and dissociation kinetics of a broad collection of HIV protease inhibitors. Quantitative models have been derived using CoMFA which relate the dissociation rate back to the chemical structures. Efforts have also been made to improve the models by systematically varying the parameters used to generate them.

A technology for optimization of potential parameters from condensed phase simulations (POP) is discussed and illustrated. It is based on direct calculations of the derivatives of macroscopic observables with respect to the potential parameters. The derivatives are used in a local minimization scheme, comparing simulated and experimental data. In particular, we show that the Newton Trust-Region protocol allows for accurate and robust optimization. POP is illustrated for a toy problem of alanine dipeptide and is applied to folding of the peptide WAAAH. The helix fraction is highly sensitive to the potential parameters while the slope of the melting curve is not. The sensitivity variations make it difficult to satisfy both observations simultaneously. We conjecture that there is no set of parameters that reproduces experimental melting curves of short peptides that are modeled with the usual functional form of a force field. We then apply the newly developed technology to study the liquid mixture of tert-butanol and water. We are able to obtain, after 4 iterations, the correct phase behavior and accurately predict the value of the Kirkwood Buff (KB) integrals. We further illustrate that a potential that is determined solely by KB information, or the pair correlation function, is not necessarily unique. / text