Protein is a fundamental material of life. There are many kinds of proteins in the body. If one of them malfunctions, it will cause physical problems. Therefore, many scientists try to analyze the functions of proteins. It is believed that the protein structure determines its function. The more similar the structures are, the more similar their functions are. Therefore, the prediction and comparison of protein structures are important topics in bioinformatics. Typically, distance RMSD (Root Mean Square Deviation) is a method used by most scientists to measure the distance between two structures. In this thesis, we propose a new algorithm to compare two protein structures, which is based on the comparison of curves in the space. To test and verify our method, we randomly choose some families in the CATH database and try to identify them. Experimental results show that our method outperforms RMSD. Furthermore, we also use the SVM (Support Vector Machine) tool to help us to obtain the better classification.

Orientador: Alexandre César Rodrigues da Silva / Resumo: Neste trabalho avaliou-se a segmentação da coluna vertebral humana utilizando imagens externas da região dorsal. A avaliação foi realizada utilizando imagens de 70 pessoas (58 mulheres e 12 homens). Essas imagens foram agrupadas por meio da associação entre a quantidade de informação existente, dado pelo valor de entropia da imagem e uma avaliação qualitativa de visibilidade da musculatura paravertebral, realizada por três avaliadores. A segmentação foi feita utilizando um algoritmo, chamado DISLo (Dorsal Image Spine Locator), que processa imagens da região dorsal baseando-se na informação visível. Após o processamento, o algoritmo DISLo produz uma imagem binária contendo uma linha de pixels de intensidade 255 que representam a coluna vertebral identificada. Aplicando o algoritmo em todas as imagens, obteve-se uma segmentação de mais de 75% da coluna vertebral na maioria dos casos (40 imagens), e na minoria (4 imagens), menos de 25%. Posteriormente, para avaliar a qualidade da segmentação, utilizou-se o RMSD (Root Mean Square Deviation) calculado entre os pixels da segmentação automática do DISLo e outra realizada de modo manual, obtida da média de 9 avaliações realizadas por três avaliadores. Pôde-se verificar que as segmentações possuem uma exatidão maior em imagens com mais entropia, bem como possuem uma diferença no RMSD de +-2 pixels quando comparadas a imagens radiográficas. Portanto, a utilização de imagens externas da região dorsal para identificação da coluna vertebr... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: In this work, the segmentation of the human vertebral column was evaluated using external images of the dorsal region. The evaluation was performed using 70 individual images (58 women and 12 men). These images were grouped using the association between the amount of existing information, given by the entropy value of the image, and a qualitative assessment of the paravertebral musculature visibility, performed by three evaluators. The segmentation was performed using an algorithm called DISLo (Dorsal Image Spine Locator) which processes images from the dorsal region, based on the visible information. After the processing, the DISLo algorithm produces a binary image containing a line with pixels having a 255 intensity value that represents the identified backbone. Applying the algorithm to all images resulted in a segmentation of more than 75% of the spine in most cases (40 images), and in the minority (4 images), less than 25%. Subsequently, to evaluate the quality of the segmentation, the RMSD (Root Mean Square Deviation) was calculated between the pixels of DISLo's automatic segmentation and a manual one, obtained from the average of 9 evaluations performed by three evaluators. It could be verified that the segmentations have a greater accuracy in images with more entropy as well as having a difference in the RMSD of +-2 pixels when compared to radiographic images. Therefore, the use of external images of the dorsal region for identification of the spine is viable, and a r... (Complete abstract click electronic access below) / Doutor

Drug repositioning applies existing drugs to new disease indications. A prerequisite for drug repurposing is drug promiscuity - a drug's ability to bind to several targets, possibly leading to side effects on the other hand. One reason for drug promiscuity is binding site similarity between (otherwise unrelated) proteins. In this thesis, a new algorithm for remote binding site similarity assessment and its application to the whole of the Protein Data Bank (PDB) is presented, forming the base for off-target identification and drug repositioning. The present thesis contributes to a long-standing debate on the reasons for drug promiscuity, being one of the pioneer studies investigating these from a protein structural point of view. Except for a small influence of flexibility, the analysis of all promiscuous drugs in the PDB revealed that drug properties are of minor importance. However, a strong correlation between promiscuity and binding site similarity of protein targets is found (r = 0.81), suggesting binding site similarity as the main reason for drug promiscuity. For 71 % of the promiscuous drugs at least one pair of their targets' binding sites is similar and for 18 % all are similar. In order to overcome issues in detection of remotely similar binding sites, a score for binding site similarity is developed: LigandRMSD measures the similarity of the aligned ligands and uncovers remote local similarities in proteins. It can be applied to arbitrary binding site alignments and also works on distinct ligands on a structural proteome scale. To answer the question on which other targets might be hit when targeting a particular protein, an all-to-all binding site alignment of 32,202 protein structures is analyzed. Of the hundreds of million possible protein pairs, 0.27 % were found to have similar binding sites. Extrapolating to the human proteome, for one human protein are 54 proteins with a similar binding site expected on average. Clearly, this is in contrast to the one drug-one target paradigm in drug development. Based on these data, disadvantageous off-targets can be uncovered and drug-repositioning candidates inferred. The enormous potential is demonstrated with the example of Viagra, proposing it for repositioning to Alzheimer's disease and prostate cancer. The findings in this thesis question the established single-target dogma in drug discovery. Drugs are triggered to modulate multiple targets simultaneously by the widespread binding site similarity. With the presented pipeline, drug targets can be reliably predicted: Starting from a target protein, additional targets are predicted based on binding site similarity and prioritized according to the resulting ligand structural overlap. Identifying drug targets helps to understand severe side effects and opens the door for drug repositioning.

A Thermotoga marítima (Tm) é uma bactéria que vive em temperaturas na faixa dos 65 até 90°C, com temperatura ótima do redor dos 80°C. A proteína TM1030 de Tm, é um regulador transcricional da família TetR (Tetracycline repressor protein) reguladores da expressão génica das proteínas TetA e TetB (Tetracycline resistance protein). Neste trabalho se rodarem 200ns de trajetória de dinâmica molecular a três temperaturas (293, 323 e 353K) da proteína TM1030 (PDB-1Z77) usando o pacote GROMACS com o potencial Amber99 e solvente explicito numa caixa cúbica com 90Å de comprimento, observando que RMSD da estrutura média da trajetória é menor em relação à estrutura cristalográfica, além disso que num primer momento esse RMSD tem uma mudança grande e que se estabiliza com uma maior velocidade nas maiores temperaturas. Também foi feito um analise de modos normais na mesma estrutura usando o mesmo potencial, mas com solvente implícito, usando o modelo GBSA, minimizando a estrutura até ter um coeficiente de força média de 6,4x10-8J·mol-1·cm-1 que assegura um bom mínimo local. Das trajetórias simuladas a partir das 6 menores frequências se achou uma relação com os movimentos observados nas dinâmicas moleculares e os esperados na transição alostérica entre as duas estruturas cristalográficas. Finalmente se calculam os fatores de temperatura das três trajetórias de dinâmica molecular, observando que seus esses fatores de temperatura aumentam com o aumento da temperatura, contrario do esperado da cristalografia onde diminuam com o aumento da temperatura do sistema. / The Thermotoga maritima (Tm) is a bacterium who can lives at temperatures of 65 to 90°C, with optimum temperature around of 80°C. The TM1030 protein of Tm is a transcriptional regulator from TetR family (Tetracycline repressor protein) regulators of gene expression of the TetA and TetB protein (Tetracycline resistance protein). In this work 200ns of molecular dynamics trajectory was run at three temperatures (293, 323 and 353K) of TM1030 protein (PDB-1Z77) using GROMACS package with Amber99 potential and explicit solvent in a cubic box with length 90A, noting that RMSD of the average structure of the trajectory is smaller with respect to the crystallographic structure, in addition, in a first time this RMSD have a large change and stabilizes at a higher speed at higher temperatures. There was also an analysis of normal modes on the same structure using the same potential, but with implicit solvent, using the GBSA model, minimizing the structure to have a medium force coefficient of 6,4x10-8J·mol-1·cm-1which ensures a good local minimum. Of the trajectories simulated from 6 lower frequencies was found a relationship with the movements observed in molecular dynamics and expected the allosteric transition between the two crystal structures. Finally was calculate the temperature factor of the three trajectories of molecular dynamics, observing their temperature factors increase with increasing temperature, contrary to expectations of crystallography which decrease with the increase of the system temperature.

A Thermotoga marítima (Tm) é uma bactéria que vive em temperaturas na faixa dos 65 até 90°C, com temperatura ótima do redor dos 80°C. A proteína TM1030 de Tm, é um regulador transcricional da família TetR (Tetracycline repressor protein) reguladores da expressão génica das proteínas TetA e TetB (Tetracycline resistance protein). Neste trabalho se rodarem 200ns de trajetória de dinâmica molecular a três temperaturas (293, 323 e 353K) da proteína TM1030 (PDB-1Z77) usando o pacote GROMACS com o potencial Amber99 e solvente explicito numa caixa cúbica com 90Å de comprimento, observando que RMSD da estrutura média da trajetória é menor em relação à estrutura cristalográfica, além disso que num primer momento esse RMSD tem uma mudança grande e que se estabiliza com uma maior velocidade nas maiores temperaturas. Também foi feito um analise de modos normais na mesma estrutura usando o mesmo potencial, mas com solvente implícito, usando o modelo GBSA, minimizando a estrutura até ter um coeficiente de força média de 6,4x10-8J·mol-1·cm-1 que assegura um bom mínimo local. Das trajetórias simuladas a partir das 6 menores frequências se achou uma relação com os movimentos observados nas dinâmicas moleculares e os esperados na transição alostérica entre as duas estruturas cristalográficas. Finalmente se calculam os fatores de temperatura das três trajetórias de dinâmica molecular, observando que seus esses fatores de temperatura aumentam com o aumento da temperatura, contrario do esperado da cristalografia onde diminuam com o aumento da temperatura do sistema. / The Thermotoga maritima (Tm) is a bacterium who can lives at temperatures of 65 to 90°C, with optimum temperature around of 80°C. The TM1030 protein of Tm is a transcriptional regulator from TetR family (Tetracycline repressor protein) regulators of gene expression of the TetA and TetB protein (Tetracycline resistance protein). In this work 200ns of molecular dynamics trajectory was run at three temperatures (293, 323 and 353K) of TM1030 protein (PDB-1Z77) using GROMACS package with Amber99 potential and explicit solvent in a cubic box with length 90A, noting that RMSD of the average structure of the trajectory is smaller with respect to the crystallographic structure, in addition, in a first time this RMSD have a large change and stabilizes at a higher speed at higher temperatures. There was also an analysis of normal modes on the same structure using the same potential, but with implicit solvent, using the GBSA model, minimizing the structure to have a medium force coefficient of 6,4x10-8J·mol-1·cm-1which ensures a good local minimum. Of the trajectories simulated from 6 lower frequencies was found a relationship with the movements observed in molecular dynamics and expected the allosteric transition between the two crystal structures. Finally was calculate the temperature factor of the three trajectories of molecular dynamics, observing their temperature factors increase with increasing temperature, contrary to expectations of crystallography which decrease with the increase of the system temperature.

Drug repositioning applies existing drugs to new disease indications. A prerequisite for drug repurposing is drug promiscuity - a drug's ability to bind to several targets, possibly leading to side effects on the other hand. One reason for drug promiscuity is binding site similarity between (otherwise unrelated) proteins. In this thesis, a new algorithm for remote binding site similarity assessment and its application to the whole of the Protein Data Bank (PDB) is presented, forming the base for off-target identification and drug repositioning. The present thesis contributes to a long-standing debate on the reasons for drug promiscuity, being one of the pioneer studies investigating these from a protein structural point of view. Except for a small influence of flexibility, the analysis of all promiscuous drugs in the PDB revealed that drug properties are of minor importance. However, a strong correlation between promiscuity and binding site similarity of protein targets is found (r = 0.81), suggesting binding site similarity as the main reason for drug promiscuity. For 71 % of the promiscuous drugs at least one pair of their targets' binding sites is similar and for 18 % all are similar. In order to overcome issues in detection of remotely similar binding sites, a score for binding site similarity is developed: LigandRMSD measures the similarity of the aligned ligands and uncovers remote local similarities in proteins. It can be applied to arbitrary binding site alignments and also works on distinct ligands on a structural proteome scale. To answer the question on which other targets might be hit when targeting a particular protein, an all-to-all binding site alignment of 32,202 protein structures is analyzed. Of the hundreds of million possible protein pairs, 0.27 % were found to have similar binding sites. Extrapolating to the human proteome, for one human protein are 54 proteins with a similar binding site expected on average. Clearly, this is in contrast to the one drug-one target paradigm in drug development. Based on these data, disadvantageous off-targets can be uncovered and drug-repositioning candidates inferred. The enormous potential is demonstrated with the example of Viagra, proposing it for repositioning to Alzheimer's disease and prostate cancer. The findings in this thesis question the established single-target dogma in drug discovery. Drugs are triggered to modulate multiple targets simultaneously by the widespread binding site similarity. With the presented pipeline, drug targets can be reliably predicted: Starting from a target protein, additional targets are predicted based on binding site similarity and prioritized according to the resulting ligand structural overlap. Identifying drug targets helps to understand severe side effects and opens the door for drug repositioning.

This thesis discusses Kaplan, a free conformer searching package, available at github.com/PeaWagon/Kaplan / Conformer searching algorithms find minima in the Potential Energy Surface (PES) of a molecule, usually by following a torsion-driven approach. The minima represent conformers, which are interchangeable via free rotation around bonds. Conformers can be used as input to computational analyses, such as drug design, that can convey molecular reactivity, structure, and function. With an increasing number of rotatable bonds, finding optima in the PES becomes more complicated, as the dimensionality explodes. Kaplan is a new, free and open-source software package written by the author that uses a ring-based Evolutionary Algorithm (EA) to find conformers. The ring, which contains population members (or pmems), is designed to allow initial PES exploration, followed by exploitation of individual energy wells, such that the most energetically-favourable structures are returned. The strengths and weaknesses of existing publicly available conformer searchers are discussed, including Balloon, RDKit, Openbabel, Confab, Frog2, and Kaplan. Since RDKit is usually considered to be the best free package for conformer searching, its conformers for the amino acids were optimised using the MMFF94 forcefield and compared to the conformers generated by Kaplan. Amino acid conformers are well characterised, and provide insight for protein substructure. Of the 20 molecules, Kaplan found a lower energy minima for 12 of the structures and tied for 5 of them. Kaplan allows the user to specify which dihedrals (by atom indices) to optimise and angles to use, a feature that is not offered by other programs. The results from Kaplan were compared to a known dataset of amino acid conformers. Kaplan identified all 57 conformers of methionine to within 1.2Å, and found identical conformers for the 5 lowest-energy structures (i.e. within 0.083Å), following forcefield optimisation. / Thesis / Master of Science (MSc) / A conformer search affords the low-energy arrangements of atoms that can be obtained via rotation around bonds. Conformers provide insight about the chemical reactivity and physical properties of a molecule. With increasing molecule size, the number of possible conformers increases exponentially. To search the space of possible conformers, this thesis presents Kaplan, which is a software package that implements a novel directed, stochastic, sampling technique based on an Evolutionary Algorithm (EA). Kaplan uses a special type of EA that stores sets of conformers in a ring-based structure. Unlike other conformer-specific packages, Kaplan provides the means to analyse and interact with found conformers. Known conformers of amino acids are used to verify Kaplan. Other tools for generating conformers are discussed, including a comparison of freely available software. Kaplan effectively finds the conformers of small molecules, but requires additional parametrisation to find the conformers of mid-sized molecules, such as Penta-Alanine.

Molecular Biology has allowed the characterization and manipulation of the molecules of life in the wet lab. Also the structures of those macromolecules are being continuously elucidated. During the last decades of the past century, there was an increasing interest to study how the different genes are organized into different organisms (‘genomes’) and how those genes are expressed into proteins to achieve their functions. Currently the sequences for many genes over several genomes have been determined. In parallel, the efforts to have the structure of the proteins coded by those genes go on. However it is experimentally much harder to obtain the structure of a protein, rather than just its sequence. For this reason, the number of protein structures available in databases is an order of magnitude or so lower than protein sequences. Furthermore, in order to understand how living organisms work at molecular level we need the information about the interaction of those proteins. Elucidating the structure of protein macromolecular assemblies is still more difficult. To that end, the use of computers to predict the structure of these complexes has gained interest over the last decades. The main subject of this thesis is the evaluation of current available computational methods to predict protein – protein interactions and build an atomic model of the complex. The core of the thesis is the evaluation protocol I have developed at Service de Conformation des Macromolécules Biologiques et de Bioinformatique, Université Libre de Bruxelles, and its computer implementation. This method has been massively used to evaluate the results on blind protein – protein interaction prediction in the context of the world-wide experiment CAPRI, which have been thoroughly reviewed in several publications [1-3]. In this experiment the structure of a protein complex (‘the target’) had to be modeled starting from the coordinates of the isolated molecules, prior to the release of the structure of the complex (this is commonly referred as ‘docking’). The assessment protocol let us compute some parameters to rank docking models according to their quality, into 3 main categories: ‘Highly Accurate’, ‘Medium Accurate’, ‘Acceptable’ and ‘Incorrect’. The efficiency of our evaluation and ranking is clearly shown, even for borderline cases between categories. The correlation of the ranking parameters is analyzed further. In the same section where the evaluation protocol is presented, the ranking participants give to their predictions is also studied, since often, good solutions are not easily recognized among the pool of computer generated decoys. An overview of the CAPRI results made per target structure and per participant regarding the computational method they used and the difficulty of the complex. Also in CAPRI there is a new ongoing experiment about scoring previously and anonymously generated models by other participants (the ‘Scoring’ experiment). Its promising results are also analyzed, in respect of the original CAPRI experiment. The Scoring experiment was a step towards the use of combine methods to predict the structure of protein – protein complexes. We discuss here its possible application to predict the structure of protein complexes, from a clustering study on the different results. In the last chapter of the thesis, I present the preliminary results of an ongoing study on the conformational changes in protein structures upon complexation, as those rearrangements pose serious limitations to current computational methods predicting the structure protein complexes. Protein structures are classified according to the magnitude of its conformational re-arrangement and the involvement of interfaces and particular secondary structure elements is discussed. At the end of the chapter, some guidelines and future work is proposed to complete the survey.

A major problem of our time is the environmental impact and socio-economic consequences of greenhouse gases, caused mainly by burning fossil fuels. In this context, hybrid porous materials known as MOFs have been investigated both experimentally and computationally for CO2 adsorption, among many other applications. The large number of atoms, however, is an obstacle to computationally expensive methods. This work evaluates the performance of semi-empirical quantum methods (usually applied only to treat organic and biological compounds) in the description of CO2 adsorption process for IRMOF-74 series. The AM1, PM3 PM6 and PM7 methods were used in the description of 72 structures, and the impact of MOZYME algorithm in calculation was also evaluated. Chemical and geometrical properties of the system were estimated considering the presence or absence of water in the structure, variations in CO2 concentration in the unit cell, primary and secondary sites occupancy, and different metal ions in the structure. The results were compared with experimental data and computational estimates obtained by DFT, emphasizing the importance of correction for dispersion forces in energy calculations. The presence of open-shell metal ions affect the calculations, but PM6 and PM7 methods are able to reproduce the geometric structure of MOFs, found that the presence of water hinders CO2 adsorption, detected the primary and secondary adsorption sites, providing estimates for the binding energy comparable to that of the most widespread computational methods and in agreement with experimental data. / Um grande problema da atualidade é o impacto ambiental e as consequências socioeconômicas decorrentes da emissão de gases, causada principalmente pela queima de combustíveis fósseis. Nesse contexto, os materiais porosos híbridos conhecidos como MOFs têm sido estudados tanto experimentalmente quanto computacionalmente para adsorção de CO2, dentre várias outras aplicações. O grande número de átomos, no entanto, é um obstáculo para métodos custosos computacionalmente. Este trabalho avalia o desempenho de métodos semiempíricos (geralmente utilizados apenas para tratar compostos orgânicos e biológicos) na descrição do processo de adsorção de CO2 pela série IRMOF-74. Os métodos AM1, PM3, PM6 e PM7 foram utilizados na descrição de até 72 estruturas, avaliando também o impacto do algoritmo MOZYME nos cálculos. Foram estimadas propriedades químicas e geométricas do sistema considerando a presença ou ausência de água na estrutura, variações na quantidade de CO2 na célula unitária, ocupação de sítios primários e secundários, e diferentes íons metálicos na estrutura. Os resultados foram comparados com dados experimentais e com estimativas computacionais obtidas por DFT, enfatizando a importância de correções para forças de dispersão nos cálculos de energia. A presença de íons metálicos com camada incompleta prejudica os cálculos, porém os métodos PM6 e PM7 reproduziram bem a estrutura cristalográfica das MOFs, identificaram que a presença de água dificulta a adsorção de CO2, detectaram os sítios de adsorção primário e secundário, obtendo estimativas para a energia de ligação comparáveis às dos métodos computacionais mais difundidos e em concordância com dados experimentais.

The goal of this thesis is test and comparation of the offline tools for prediction of protein structure and creation of metaprediktor, which allows the user to select the appropriate tool, according to given parameters. Testing tool is based on a dataset of proteins, which is based on the SCOP database and it is trying to be as balanced as possible to include proteins from different families and thus could best evaluate individual tools. The results of this thesis are requirements of metaprediktor and also which data and settings can be allowed and processed and how it will be implemented.