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

While there have been many recently developed Elastic Network Models (ENM) to calculate the fluctuation dynamics of proteins, e.g., Gaussian Network Model (GNM), Anisotropic Network Model (ANM), Distance Network Model (DNM), the concept of loading these models to study the molecular mechanics and constitutive behavior of structural proteins has remained relatively untouched, until very recently. This work entails using the ANM as the framework for developing a finite element model of a 9–monomer strand of actin. Critical input parameters to the model, such as the cutoff radius, r[subscript c], and spring constant, k, are generated by matching the all-atom steered molecular dynamics (SMD) residue displacements to that of the ANM. The parameters yielding the best match between the SMD and structural ENM (SENM) simulations will then be input into the finite element model (FEM) for a more in depth analysis. The finite element model incorporates a 9–monomer strand of actin. The F–actin strand is subjected axial and torsional loads comparable to those seen in vivo. Key areas of interest in the protein are examined, such as the nucleotide binding pocket (NBP) and the DNase I binding loop, to demonstrate how loading affects the protein’s conformation. Local residue displacements are tracked in an effort to garner a better understanding of how various loads are transmitted through F–actin during key events. Insights and conclusions are discussed along with the implications of this work. / text

abstract: This thesis explores a wide array of topics related to the protein folding problem, ranging from the folding mechanism, ab initio structure prediction and protein design, to the mechanism of protein functional evolution, using multi-scale approaches. To investigate the role of native topology on folding mechanism, the native topology is dissected into non-local and local contacts. The number of non-local contacts and non-local contact orders are both negatively correlated with folding rates, suggesting that the non-local contacts dominate the barrier-crossing process. However, local contact orders show positive correlation with folding rates, indicating the role of a diffusive search in the denatured basin. Additionally, the folding rate distribution of E. coli and Yeast proteomes are predicted from native topology. The distribution is fitted well by a diffusion-drift population model and also directly compared with experimentally measured half life. The results indicate that proteome folding kinetics is limited by protein half life. The crucial role of local contacts in protein folding is further explored by the simulations of WW domains using Zipping and Assembly Method. The correct formation of N-terminal β-turn turns out important for the folding of WW domains. A classification model based on contact probabilities of five critical local contacts is constructed to predict the foldability of WW domains with 81% accuracy. By introducing mutations to stabilize those critical local contacts, a new protein design approach is developed to re-design the unfoldable WW domains and make them foldable. After folding, proteins exhibit inherent conformational dynamics to be functional. Using molecular dynamics simulations in conjunction with Perturbation Response Scanning, it is demonstrated that the divergence of functions can occur through the modification of conformational dynamics within existing fold for β-lactmases and GFP-like proteins: i) the modern TEM-1 lactamase shows a comparatively rigid active-site region, likely reflecting adaptation for efficient degradation of a specific substrate, while the resurrected ancient lactamases indicate enhanced active-site flexibility, which likely allows for the binding and subsequent degradation of different antibiotic molecules; ii) the chromophore and attached peptides of photocoversion-competent GFP-like protein exhibits higher flexibility than the photocoversion-incompetent one, consistent with the evolution of photocoversion capacity. / Dissertation/Thesis / Ph.D. Physics 2014

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 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.

A abordagem de modos normais de baixa frequência na descrição das flutuações conformacionais dos estados nativos das proteínas globulares tem ajudado na caracterização das suas funções biológicas. Vários métodos teóricos e experimentais têm sido empregados para a determinação destas flutuações internas. Estes movimentos podem ser caracterizados pelo fator Debye-Waller (fator-B), correspondente à mobilidade local do resíduo em nível atômico. A análise de modos normais utilizando os modelos de rede elástica (ENM) demonstra ser uma técnica robusta. Fatores-B experimentais são reproduzidos teoricamente por meio desta técnica em tempos computacionais relativamente curtos, mostrando-se competitiva com as técnicas mais sofisticadas. O modelo de rede elástica é uma abordagem t ipo coarse-grain na qual a proteína no seu estado enovelado é representada por uma rede elástica tridimensional de carbonos conectados por molas. As molas representam as interações ligantes e não ligantes entre os carbonos . Neste trabalho, inicialmente, estudamos os modelos de rede elástica já conhecidos na literatura. Em seguida, realizamos um estudo comparativo entre eles. Neste estudo, comprovamos que os modelos pfGNM e pfANM apresentam melhor correlação com os fatores-B experimentais que os os modelos GNM e ANM tradicionais. Desenvolvemos também uma nova abordagem, a qual intitulamos número de contatos ponderados anisotrópica (AWCN). Mostramos que a abordagem AWCN apresenta um desempenho significativamente melhor que o modelo de rede elástica anisotrópica tradicional. Por fim, realizamos um estudo de caráter investigativo do comportamento do peso das interações entre resíduos. Este estudo re velou que, para os modelos WCN e AWCN, a correlação exibe o seu valor máximo para interações ponderadas $1/R^p$, entre resíduos $i$ e $j$j, para valores de $p$ em torno de 2. Nos modelos pfGNM e pfANM a correlação é maximizada para dois valores de $p$, o primeiro em torno de 2 e o segundo em torno de 4,75, indicando que a ponderação pelo recíproco do quadrado da distância, usualmente empregada na literatura, pode não ser adequada para obter a melhor correlação. / Low frequency normal mode approach to describe conformational fluctuations of globular proteins has helped to characterize their biological functions. Various theoretical and experimental methods have been employed to det ermine the magnitudes of those internal motions. Those motions can be characterized by the Debye-Waller factor (B-factor), co rresponding to the local mobility of the residue at the atomic level. Normal mode analysis using elastic network models (ENM) has demonstrated to be a robust technique. Experimental B-factors has been reproduced theoretically by means of this techniq ue in a short computational time and it has been shown to be competitive with more sophisticated techniques. The ENM is a coarse-grained approach in which the protein is represented by a three-dimensional elastic network of alpha-carbon atoms connect ed by springs. Springs represent bonded and non-bonded interactions between the alpha-carbon atoms. In this work, we study th e elastic network models known in the literature. Next, we perform a comparative study between them. We show that the pfGNM a nd pfANM models present better correlation with experimental B-factors than the traditional GNM and ANM models. We also devel op a new approach, which we entitled anisotropic weighted contact number (AWCN). We show that it presents results significantly better than the traditional anisotropic elastic network model. Finally, we perform a study of investigative character of the behavior for the weight of the interactions between residues. This study revealed that, for the WCN and AWCN models, the correlation exhibits its maximum value for weighted interactions $1/R^p$, between residues $i$ and $j$, for values of $p$ around 2. In the pfGNM and pfANM models the correlation is max imized for two values of $p$, the first one around 2 and the second one around 4.75. This indicates that the weighting by the reciprocal of the square of the distance, usually employed in the literature, may not be appropriate to obtain the best correlation.

A abordagem de modos normais de baixa frequência na descrição das flutuações conformacionais dos estados nativos das proteínas globulares tem ajudado na caracterização das suas funções biológicas. Vários métodos teóricos e experimentais têm sido empregados para a determinação destas flutuações internas. Estes movimentos podem ser caracterizados pelo fator Debye-Waller (fator-B), correspondente à mobilidade local do resíduo em nível atômico. A análise de modos normais utilizando os modelos de rede elástica (ENM) demonstra ser uma técnica robusta. Fatores-B experimentais são reproduzidos teoricamente por meio desta técnica em tempos computacionais relativamente curtos, mostrando-se competitiva com as técnicas mais sofisticadas. O modelo de rede elástica é uma abordagem t ipo coarse-grain na qual a proteína no seu estado enovelado é representada por uma rede elástica tridimensional de carbonos conectados por molas. As molas representam as interações ligantes e não ligantes entre os carbonos . Neste trabalho, inicialmente, estudamos os modelos de rede elástica já conhecidos na literatura. Em seguida, realizamos um estudo comparativo entre eles. Neste estudo, comprovamos que os modelos pfGNM e pfANM apresentam melhor correlação com os fatores-B experimentais que os os modelos GNM e ANM tradicionais. Desenvolvemos também uma nova abordagem, a qual intitulamos número de contatos ponderados anisotrópica (AWCN). Mostramos que a abordagem AWCN apresenta um desempenho significativamente melhor que o modelo de rede elástica anisotrópica tradicional. Por fim, realizamos um estudo de caráter investigativo do comportamento do peso das interações entre resíduos. Este estudo re velou que, para os modelos WCN e AWCN, a correlação exibe o seu valor máximo para interações ponderadas $1/R^p$, entre resíduos $i$ e $j$j, para valores de $p$ em torno de 2. Nos modelos pfGNM e pfANM a correlação é maximizada para dois valores de $p$, o primeiro em torno de 2 e o segundo em torno de 4,75, indicando que a ponderação pelo recíproco do quadrado da distância, usualmente empregada na literatura, pode não ser adequada para obter a melhor correlação. / Low frequency normal mode approach to describe conformational fluctuations of globular proteins has helped to characterize their biological functions. Various theoretical and experimental methods have been employed to det ermine the magnitudes of those internal motions. Those motions can be characterized by the Debye-Waller factor (B-factor), co rresponding to the local mobility of the residue at the atomic level. Normal mode analysis using elastic network models (ENM) has demonstrated to be a robust technique. Experimental B-factors has been reproduced theoretically by means of this techniq ue in a short computational time and it has been shown to be competitive with more sophisticated techniques. The ENM is a coarse-grained approach in which the protein is represented by a three-dimensional elastic network of alpha-carbon atoms connect ed by springs. Springs represent bonded and non-bonded interactions between the alpha-carbon atoms. In this work, we study th e elastic network models known in the literature. Next, we perform a comparative study between them. We show that the pfGNM a nd pfANM models present better correlation with experimental B-factors than the traditional GNM and ANM models. We also devel op a new approach, which we entitled anisotropic weighted contact number (AWCN). We show that it presents results significantly better than the traditional anisotropic elastic network model. Finally, we perform a study of investigative character of the behavior for the weight of the interactions between residues. This study revealed that, for the WCN and AWCN models, the correlation exhibits its maximum value for weighted interactions $1/R^p$, between residues $i$ and $j$, for values of $p$ around 2. In the pfGNM and pfANM models the correlation is max imized for two values of $p$, the first one around 2 and the second one around 4.75. This indicates that the weighting by the reciprocal of the square of the distance, usually employed in the literature, may not be appropriate to obtain the best correlation.

This study proposed a finite element procedure for protein normal mode analysis (NMA). The finite element model adopted the protein solvent-excluded surface to generate a homogeneous and isotropic volume. A simplified triangular approximation of coarse molecular surface was generated from the original surface model by using the Gaussian-based blurring technique. Similar to the widely adopted elastic network model, the finite element model holds a major advantage over standard all-atom normal mode analysis: the computationally expensive process of energy minimization that may distort the initial protein structure has been eliminated. This modification significantly increases the efficiency of normal mode analysis. In addition, the finite element model successfully brings out the capability of normal mode analysis in low-frequency/high collectivity molecular motion by capturing protein shape properties. Fair results from six protein models in this study have fortified the capability of the finite element model in protein normal mode analysis.