Submitted by FABIANA DA SILVA FRANÇA (fabiana21franca@gmail.com) on 2017-11-09T14:11:12Z No. of bitstreams: 2 arquivototal.pdf: 4984003 bytes, checksum: 15bf30b6c475733d92d8611db7b64bc0 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-11-09T14:11:12Z (GMT). No. of bitstreams: 2 arquivototal.pdf: 4984003 bytes, checksum: 15bf30b6c475733d92d8611db7b64bc0 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-11-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Ricinus communis, specifically fruit, castor bean, has gained industry, media and government attention due to the derivate properties such as ricin and castor oil. The total fatty acids xtracted from castor bean, 90% comprises ricinoleic acid. Beyond the castor oil, castor bean processing generate co-products: fruit peel and the castor cake. These co-products present significant amounts of fibers and proteins, in addition to a potential use as nematicide. Since world production of castor oil is 1.5 million tons per year and the proportion of co-product production with castor oil production is approximately 1:1, so, it’s essential to find an economically viable destination for these co-products. In addition to the destinations used, a route with high commercial value would be the use as animal feed, however, this does not occur due to the presence of ricin. Ricin its a ribosome inactivating protein present in the castor bean seed, that consists of two subunits, RTA and RTB, with the RTA being the catalytic subunit. In addition this problem, ricin is used as a biological weapon by terrorists and activists. The inhibition of mechanism of ricin action has biotechnological interest, where RTA is the target for inhibitors synthesis. In this context, the method used to search for new inhibitors its Molecular Docking. This method evaluates thousands of ligands in a short time, however, presents low accuracy in the prediction of binding affinity. In this perspective, SMD simulations can be used. This method is based on the correlation of the mean force profile needed to decouple the ligand from the protein with its affinity. The scientific literature has reported promising results with the approach to discern active binders from inactive. In the present study, beyond the validation data from known RTA inhibitors, the binding affinity potential of 6 novel structures to form complexes with RTA using SMD simulations was evaluated. It was necessary to use Molecular Docking and Molecular Dynamics aproaches to obtain and refine new RTA complexes with novel ligands and next submitted to SMD simulation (k = 2 kcal/mol/Ų, v = 0.005 nm/ps). In addition to the mean force profile obtained from multiple independent SMD simulations, the rupture force and the average of pulling work were measured, these being a direct relation with the binding affinity. In summary, the results of the validation of rupture force and average of pulling work with the experimental data showed a correlation and determination coefficient with R = -0.992 and R² = 0.984 (rupture force) and R -0.958 and R² = 0.918 (average of pulling work), respectively. By means of these validations, the evaluation of novel structures using SMD simulations showed that 4 of the 6 proposed structures present in silico, binding affinity potential for RTA. / A Ricinus communis, especificamente o seu fruto, a mamona, tem ganhado atenção da indústria, mídia e governos, devido as propriedades de seus derivados, especialmente a ricina e o óleo. Do total de ácidos graxos extraídos da mamona, 90% compreende ao ácido ricinoleico. Além do óleo, os coprodutos gerados durante a produção tem ganhado bastante atenção, sendo os mais importantes, a casca do fruto e a torta. Tais coprodutos apresentam quantidades significativas de fibras e proteínas, além de um potencial uso como nematicida. Uma vez que a produção mundial de óleo de mamona é de 1,5 milhão de toneladas-ano e a proporção de produção de coprodutos com a produção de óleo é de aproximadamente 1:1, é fundamental encontrar um destino economicamente viável para esses coprodutos. Além dos destinos utilizados, uma rota com alto valor comercial seria o uso como ração animal, entretanto, isto não ocorre devido a presença da ricina. Esta, corresponde a uma proteína inativadora de ribossomos presente na semente da mamona, e é constituída por duas subunidades, a RTA e RTB, sendo a RTA a subunidade catalítica. Além da problemática supracitada, a ricina é utilizada como arma biológica por terroristas e ativistas, logo, a inibição do mecanismo de ação desta proteína é de grande interesse biotecnológico, sendo a RTA o alvo para síntese de inibidores. Neste aspecto, o método utilizado para busca de novos inibidores é o Atracamento Molecular (Molecular Docking). Este método avalia milhares de ligantes num curto intervalo de tempo, entretanto, apresenta baixa acurácia na predição da afinidade de ligação. Nesta perspectiva, a Dinâmica Molecular Induzida (SMD), pode ser utilizada. Este método baseia-se na correlação do perfil de força médio necessário para desacoplar o ligante da proteína com a sua afinidade. A literatura científica tem relatado resultados promissores no uso dessa abordagem para discernir ligantes ativos de inativos. No presente estudo, além de validações com inibidores de RTA conhecidos, foi avaliado o potencial de afinidade de 6 estruturas inéditas a formarem complexos com a RTA utilizando simulações SMD. Para isso, foi necessário recorrer a abordagens de Atracamento Molecular e Dinâmica Molecular para obtenção e refinamento de novos complexos da RTA com os ligantes candidatos, para somente então, serem submetidos a simulação SMD (k = 2 kcal/mol/Ų, v = 0.005 nm/ps). Neste passo, além do perfil de força médio obtido a partir de múltiplas simulações SMD independentes, foi mensurado a força de ruptura e o trabalho médio realizado pela força, este último, apresenta uma relação direta com a afinidade de ligação pela igualdade de Jarzynski. Em suma, resultados de validação da força de ruptura e trabalho com os dados experimentais mostraram coeficiente de correlação e determinação com R = -0.992 e R² = 0.984 (força de ruptura) e R -0.958 e R² = 0.918 (perfil médio do trabalho), respectivamente. Por meio destas validações, a avaliação dos ligantes candidatos utilizando simulações SMD mostrou que 4 das 6 estruturas propostas, apresentam in silico, potencial de afinidade de ligação para com a RTA.

In this thesis, we have advanced a set of distinct bioinformatic and computational tools to address the structure and function of proteins. Using data mining of the protein data bank (PDB), we have collected statistics connecting the propensity between the protein sequence and the secondary structure. This new tool has enabled us to evaluate new structures as well as a family of structures. A comparison of the wild type staphylococcal nuclease to various mutants using the proposed tool has indicated long-range conformational deviations spatially distant from the mutation point. The energetics of protein unfolding has been studied in terms of the forces observed in molecular dynamics simulations. An adaptive integration of the steered molecular dynamics is proposed to reduce ground state dominance by the rare low energy trajectories on the estimated free energy profile. The proposed adaptive algorithm is utilized to reproduce the potential of mean force of the stretching of decaalanine in vacuum at lower computational cost. It is then used to construct the potential of mean force of this transition in solvent for the first time as to observe the hydration effect on the helix-coil transformation. Adaptive steered molecular dynamics is also implemented to obtain the free energy change during the unfolding of neuropeptide Y and to confirm that the monomeric form of neuropeptide Y adopts halical-hairpin like pancreatic-polypeptide fold.

Collagen is the most abundant protein in mammals and has special mechanical behavior that enables it to play an important role in the structural integrity of many tissues, e.g., skin, tendon, bone, cartilage and blood vessels. The mechanical properties of collagen are governed by hierarchical mechanisms in different length-scales from molecule to tissue level. Currently, there is no multi-scale model that can predict the mechanical properties of collagen at macroscopic length scales from the behavior of microstructural elements at smaller length scales. This dissertation aimed at developing a multi-scale model using a bottom-up approach to predict the elastic and viscoelastic behaviors of collagen at length scales spanning from nano to microscale. Creep simulations were performed using steered molecular dynamics (SMD) method on collagen molecules, cross-link, and micro-fibrils with various lengths. A micro-fibril is considered as a combination of two collagen molecules connected by a cross-link. The strain time histories for force levels in the range of 10 to 4000 pN were characterized using quasilinear viscoelastic models. These models were utilized to make a reduced model of a micro-fibril and the reduced models, in turn, were combined to make a model of a fibril up to 300 micrometers in length. The micro-fibril and fibril models were validated with available experimental measurements. Hydrogen bonds rupture and formation of collagen molecule played a central role in its viscoelastic behavior and were used to estimate the creep growth rate. The propagation of force wave in the molecule was shown to be an important factor in providing the time-dependent properties of the fibrils. This propagation was modeled with delay elements and this allowed reducing the micro-fibril model to only three degrees of freedom. In conclusion, the results confirmed that the combination of molecular dynamics simulations and viscoelastic theory could be successfully utilized to investigate the viscoelastic behavior of collagen at small scales. The model reported in this dissertation, lays the groundwork for future studies on collagen, particularly in elucidating how each particular level of hierarchy affects the overall tissue behavior. / Mechanical Engineering

Neste trabalho foram utilizadas técnicas de modelagem molecular computacional para descrever nanossuperfícies funcionalizadas com biomoléculas do sistema imunológico correlacionando resultados experimentais obtidos com o microscópio de força atômica, simulações de dinâmica molecular e dinâmica molecular direcionada. O objetivo principal proposto é avaliar as forças intermoleculares provenientes das interações antígeno-anticorpo (funcionalizados em nanossuperfícies) para aplicação no desenvolvimento de nanoimunossensores e detecção de doenças desmielinizantes, como a Neuromielite Óptica. A Neuromielite Óptica é uma doença inflamatória autoimune na qual o próprio sistema imunológico reage contra os nervos ópticos e a medula espinhal, causando lesão desmielinizante. Estudos na literatura estabeleceramo anticorpo anti-aquaporina4 como um importante biomarcador da doença. Neste contexto, um nanoimmunosensorvem sendo desenvolvido com a técnica de Microscopia de Força Atômica, o qual visa detectar o anticorpoanti-aquaporina4 no soro de portadores da doença. Tal estudo necessitou de uma nova abordagem computacional para a descrição de estruturas tridimensionais de anticorpos. Essa nova aproximação consistiu na aplicação de técnicas de computacionais de modelagem e engenharia molecular para a geração de modelos de anticorpos com base em sucessivas substituições dos resíduos componentes do sítio de interação com o antígeno. Testes realizados envolvendo modelos de anticorpos disponíveis em bancos de dados especializadosindicaram (48 ± 18) % e (65 ± 14) % de identidade das cadeias leve e pesada, respectivamente, entre os modelos gerados computacionalmente e as estruturas 3D reais de anticorpos. Por fim, para comprovar o funcionamento dos nanoimunossensores, foi desenvolvido um modelo estatístico para tratar e interpretar os dados experimentais. Este modelo foi eficiente para distinguir os pacientes soropositivos de sujeitos soronegativos para determinados biomarcadores relacionados à Neuromielite Óptica e a Esclerose Múltipla, fornecendo assim um novo e mais preciso processopara diagnóstico de doenças desmielinizantes. / Study of the biological coverage of nanosurfaces by computational modeling: application in the development of nanoimunoresensors. In this work, computational molecular modeling techniques were applied to describe nanosurfaces functionalized with immune system biomolecules, correlating data from atomic force microscope experiments, molecular dynamics, and steered molecular dynamics simulations. The main goal of this research was to evaluate intermolecular forces involved in the antigen-antibody interaction on the nanosurfaces during the development of nanoimmunosensors for demyelinating diseases detection, especially neuromyelitisoptica. The neuromyelitisoptica is an autoimmune inflammation in which components of the immune system respond against optical nerves and spinal cord, resulting in demyelinating lesions. In the literature, studies have established anti-aquaporin 4 as an important biomarker for neuromyelitisoptica. Then, a nanoimmunosensor for anti-aquaporin 4 antibodies detection in neuromyelitisoptica patients serum via Atomic Force Microscopy is in development. This study requested a computational approach for describing the tridimensional structure of antibodies. The novel approach consisted of computer molecular modeling and engineering to perform successive substitutions in residues of the antigen interaction site. Tests carried out using antibody structures available in specialized data banks demonstrated the similarity of (48 ± 18) % and (65 ± 14) % for light and heavy chains, respectively, of the computationally generated models and experimental 3D structures of antibodies. Additionally, a statistical model was developed to prove the nanoimmunosensor sensing activity, which was useful to treat and interpret the experimental data. This statistical model was efficient to distinguish seropositive patients from seronegative subjects considering specific biomarkers related to neuromyelitisoptica and multiple sclerosis, providing a novel and more precise process for demyelinating disease diagnosis.

<p>Ankyrin repeats (ARs) are polypeptide motifs identified in thousands of proteins. Many AR proteins play a function as scaffolds in protein-protein interactions which may require specific mechanical properties. Also, a number of AR proteins have been proposed to mediate mechanotransduction in a variety of different functional settings. The folding and stability of a number of AR proteins have been studied in detail by chemical and temperature denaturation experiments, yet the mechanic of AR proteins remain largely unknown. In this dissertation, we have researched the mechanical properties of AR proteins by using protein engineering and a combination of atomic force microscopy (AFM)-based single-molecule force spectroscopy and steered molecular dynamics (SMD) simulations. Three kinds of AR proteins were investigated: NI6C (synthetic AR protein), D34 (of ankyrin-R) and gankyrin (oncoprotein). While the main focus of this research was to characterize the response of AR proteins to mechanical forces, our results extended beyond the protein nanomechanics to the understanding of protein folding mechanisms.</p> / Dissertation

Molecular dynamics (MD) is a powerful tool that can be applied to protein folding and protein structure. MD allows for the calculation of movement, and final position, of atoms in a biomolecule. These movements can be used to investigate the pathways that allow proteins to fold into energetically favorable structures. While MD is very useful, it still has its limitations. Most notable, computing power and time are of constant concern. Protein structure is inherently important due to the direct link between the structure of a protein and its function. One of the four levels of protein structure, the secondary structure, is the first level to accommodate for the three-dimensional shape of a protein. The main driving force behind secondary structure is hydrogen bonding, which occurs between the carboxyl oxygen and the amine hydrogen of the backbone of a peptide. Determining a greater link between hydrogen bond patterns and types of secondary structure can provide more insight on how proteins fold. Because molecular dynamics allows for an atomic level view of the dynamics behind protein folding/unfolding, it becomes very useful in observing the effects of particular hydrogen bond patterns on the folding pathway and final structure formed of a protein. Using molecular dynamic simulations, a series of experiments in an attempt to alter structure, hydrogen bonding, and folding patterns, can be performed. This information can be used to better understand the driving force of secondary structure, and use the knowledge gained to manipulate these simulations to force folding events, and with that, desired secondary structure features.

<p>Understanding and predicting chemical phenomena is the main goal of computational chemistry. In this thesis I present my work on applying computational approaches to study chemical processes in biological and supramolecular systems.</p> <p>pH-responsive molecular tweezers have been proposed as an approach for targeting drug-delivery to tumors, which tend to have a lower pH than normal cells. In chapter 2 I present a computational study I performed on a pH-responsive molecular tweezer using <em>ab initio</em> quantum chemistry in the gas phase and molecular dynamics simulations in solution. The binding free energy in solution was calculated using Steered Molecular Dynamics. We observe, in atomistic detail, the pH-induced conformational switch of the tweezer and the resulting release of the drug molecule. Even when the tweezer opens, the drug molecule remains near a hydrophobic arm of the molecular tweezer. Drug release cannot occur, it seems, unless the tweezer is a hydrophobic environment with low pH.</p> <p>The protonation state of amino acid residues in proteins depends on their respective pK_a values. Computational methods are particularly important for estimating the pK_a values of buried and active site residues, where experimental data is scarce. In chapter 3 I used the cluster model approach to predict the pK_a of some challenging protein residues and for which methods based on the numerical solution of the Poisson-Boltzmann equation and empirical approaches fail. The ionizable residue and its close environment were treated quantum mechanically, while the rest of the protein was replaced by a uniform dielectric continuum. The approach was found to overestimate the electrostatic interaction leading to predicting lower pK_a values.</p> / Master of Science (MSc)