Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Recent advances in sequencing technology have given access to information extracted on a single cell level. Single cell RNA sequencing enables for transcriptomes to be sequenced, allowing for studies within and between cell types. A recently developed protocol, based on Smart-seq2, and the Proximity ligation essay, allows for the detection of protein data from single cells, in parallel with RNA. The combination of the transcriptomic and proteomic data will enhance researchers’ ability to explore cell states. In this study, we are comparing a new pulldown protocol with the widely-used Smart-seq2, as well as against FACS sorted cells. Our results show differences in the RNA sequenced between the two protocols, as well the prediction of cell cycle state based on their data. Using RNA extracted from the pulldown protocol in different time points, we also calculate the direction of development for the cells. We expect that the incorporation of proteomic data will shed light to relevant biological questions related to the cell function.

Estudos teóricos dos mecanismos moleculares responsáveis pela formação e estabilidade de complexos moleculares vêm ganhando relevância pelas possibilidades práticas que oferecem, por exemplo, na compreensão de diversas doenças e no desenho racional de fármacos. Neste projeto, nossa ênfase está no estudo de complexos de proteínas, extraídos do banco de dados de proteínas (PDB), onde desenvolvemos ferramentas computacionais as quais permitem efetuar análises em duas direções: 1) efetuar previsões básicas, através do emprego de propriedades eletrostáticas de proteínas, em diferentes condições e níveis preditivos e 2) realização de um conjunto de análises estatísticas, como freqüência de contato, em busca de preditores de complexos de proteínas e identificar padrões de interação entre seus aminoácidos em função da distância de separação. Com base nos resultados obtidos por ambos os estudos, objetivamos quantificar as forças físicas envolvidas na formação dos complexos protéicos. O foco do projeto, a longo prazo, é prever o fenômeno da complexação através da fusão dessas duas linhas de estudos: preditor básico de complexos protéicos e análise do potencial estatístico entre os aminoácidos que formam o complexo. O presente projeto é concluído com a construção de portais web que disponibilizarão os resultados obtidos por nossos trabalhos bem como a possibilidade de qualquer usuário, efetuar consultas por propriedades de proteínas e/ou grupo de proteínas. / Theoretical studies of the molecular mechanisms responsible for the formation and stability of molecular complexes are gaining relevance for the practical possibilities that they offer, for example, in the understanding of diverse diseases and rational drug design. In this project, our emphasis is on the study of protein complexes, extracted from protein data bank (PDB). We have developed computational tools which allow to perform analyses in two directions: 1) to make basic complexation forecasts, through the use of electrostatics properties of proteins, in different conditions and predictive levels, and 2) to carry out a set of statistical analyses, as contacts frequency, in order to build up predictor of protein complexes and to identify patters of interactions between the amino acids as a function of their separation distance. Based on the results obtained on both studies, we aim quantify the physical forces involved in the formation of protein complexes. The focus of the project, in the long run, is to foresee the phenomenon of the protein complexes through the fusing of these two study lines: a coarse-grained predictor of protein complexes and analysis of the statistical potentials between the amino acids that form the complex. The present project is concluded with the construction of web services where we make available the results obtained on our works. This server also has the possibility to be used by any computer user, that wishes to perform search on protein and/or protein group properties

Estudos teóricos dos mecanismos moleculares responsáveis pela formação e estabilidade de complexos moleculares vêm ganhando relevância pelas possibilidades práticas que oferecem, por exemplo, na compreensão de diversas doenças e no desenho racional de fármacos. Neste projeto, nossa ênfase está no estudo de complexos de proteínas, extraídos do banco de dados de proteínas (PDB), onde desenvolvemos ferramentas computacionais as quais permitem efetuar análises em duas direções: 1) efetuar previsões básicas, através do emprego de propriedades eletrostáticas de proteínas, em diferentes condições e níveis preditivos e 2) realização de um conjunto de análises estatísticas, como freqüência de contato, em busca de preditores de complexos de proteínas e identificar padrões de interação entre seus aminoácidos em função da distância de separação. Com base nos resultados obtidos por ambos os estudos, objetivamos quantificar as forças físicas envolvidas na formação dos complexos protéicos. O foco do projeto, a longo prazo, é prever o fenômeno da complexação através da fusão dessas duas linhas de estudos: preditor básico de complexos protéicos e análise do potencial estatístico entre os aminoácidos que formam o complexo. O presente projeto é concluído com a construção de portais web que disponibilizarão os resultados obtidos por nossos trabalhos bem como a possibilidade de qualquer usuário, efetuar consultas por propriedades de proteínas e/ou grupo de proteínas. / Theoretical studies of the molecular mechanisms responsible for the formation and stability of molecular complexes are gaining relevance for the practical possibilities that they offer, for example, in the understanding of diverse diseases and rational drug design. In this project, our emphasis is on the study of protein complexes, extracted from protein data bank (PDB). We have developed computational tools which allow to perform analyses in two directions: 1) to make basic complexation forecasts, through the use of electrostatics properties of proteins, in different conditions and predictive levels, and 2) to carry out a set of statistical analyses, as contacts frequency, in order to build up predictor of protein complexes and to identify patters of interactions between the amino acids as a function of their separation distance. Based on the results obtained on both studies, we aim quantify the physical forces involved in the formation of protein complexes. The focus of the project, in the long run, is to foresee the phenomenon of the protein complexes through the fusing of these two study lines: a coarse-grained predictor of protein complexes and analysis of the statistical potentials between the amino acids that form the complex. The present project is concluded with the construction of web services where we make available the results obtained on our works. This server also has the possibility to be used by any computer user, that wishes to perform search on protein and/or protein group properties

L’objectif de cette thèse est de proposer un Alphabet Structural (AS) permettant une caractérisation fine et précise des structures tridimensionnelles (3D) des protéines, à l’aide des chaînes de Markov cachées (HMM) qui permettent de prendre en compte la logique issue de l’enchaînement des fragments structuraux en intégrant l’augmentation des conformations 3D des structures protéiques désormais disponibles dans la banque de données de la Protein Data Bank (PDB). Nous proposons dans cette thèse un nouvel alphabet, améliorant l’alphabet structural HMM-SA27,appelé SAFlex (Structural Alphabet Flexibility), dans le but de prendre en compte l’incertitude des données (données manquantes dans les fichiers PDB) et la redondance des structures protéiques. Le nouvel alphabet structural SAFlex obtenu propose donc un nouveau modèle d’encodage rigoureux et robuste. Cet encodage permet de prendre en compte l’incertitude des données en proposant trois options d’encodages : le Maximum a posteriori (MAP), la distribution marginale a posteriori (POST)et le nombre effectif de lettres à chaque position donnée (NEFF). SAFlex fournit également un encodage consensus à partir de différentes réplications (chaînes multiples, monomères et homomères) d’une même protéine. Il permet ainsi la détection de la variabilité structurale entre celles-ci. Les avancées méthodologiques ainsi que l’obtention de l’alphabet SAFlex constituent les contributions principales de ce travail de thèse. Nous présentons aussi le nouveau parser de la PDB (SAFlex-PDB) et nous démontrons que notre parser a un intérêt aussi bien sur le plan qualitatif (détection de diverses erreurs)que quantitatif (rapidité et parallélisation) en le comparant avec deux autres parsers très connus dans le domaine (Biopython et BioJava). Nous proposons également à la communauté scientifique un site web mettant en ligne ce nouvel alphabet structural SAFlex. Ce site web représente la contribution concrète de cette thèse alors que le parser SAFlex-PDB représente une contribution importante pour le fonctionnement du site web proposé. Cette caractérisation précise des conformations 3D et la prise en compte de la redondance des informations 3D disponibles, fournies par SAFlex, a en effet un impact très important pour la modélisation de la conformation et de la variabilité des structures 3D, des boucles protéiques et des régions d’interface avec différents partenaires, impliqués dans la fonction des protéines / The purpose of this PhD is to provide a Structural Alphabet (SA) for more accurate characterization of protein three-dimensional (3D) structures as well as integrating the increasing protein 3D structure information currently available in the Protein Data Bank (PDB). The SA also takes into consideration the logic behind the structural fragments sequence by using the hidden Markov Model (HMM). In this PhD, we describe a new structural alphabet, improving the existing HMM-SA27 structural alphabet, called SAFlex (Structural Alphabet Flexibility), in order to take into account the uncertainty of data (missing data in PDB files) and the redundancy of protein structures. The new SAFlex structural alphabet obtained therefore offers a new, rigorous and robust encoding model. This encoding takes into account the encoding uncertainty by providing three encoding options: the maximum a posteriori (MAP), the marginal posterior distribution (POST), and the effective number of letters at each given position (NEFF). SAFlex also provides and builds a consensus encoding from different replicates (multiple chains, monomers and several homomers) of a single protein. It thus allows the detection of structural variability between different chains. The methodological advances and the achievement of the SAFlex alphabet are the main contributions of this PhD. We also present the new PDB parser(SAFlex-PDB) and we demonstrate that our parser is therefore interesting both qualitative (detection of various errors) and quantitative terms (program optimization and parallelization) by comparing it with two other parsers well-known in the area of Bioinformatics (Biopython and BioJava). The SAFlex structural alphabet is being made available to the scientific community by providing a website. The SAFlex web server represents the concrete contribution of this PhD while the SAFlex-PDB parser represents an important contribution to the proper function of the proposed website. Here, we describe the functions and the interfaces of the SAFlex web server. The SAFlex can be used in various fashions for a protein tertiary structure of a given PDB format file; it can be used for encoding the 3D structure, identifying and predicting missing data. Hence, it is the only alphabet able to encode and predict the missing data in a 3D protein structure to date. Finally, these improvements; are promising to explore increasing protein redundancy data and obtain useful quantification of their flexibility

The Protein Data Bank (PDB) is the definitive electronic repository for experimentally-derived protein structures, composed mainly of those determined by X-ray crystallography. Approximately 200 new structures are added weekly to the PDB, and at the time of writing, it contains approximately 97,000 structures. This represents an expanding wealth of high-quality information but there seem to be few bioinformatics tools that consider and analyse these data as an ensemble. This thesis explores the development of three efficient, fast algorithms and software implementations to study protein structure using the entire PDB. The first project is a crystal-form matching tool that takes a unit cell and quickly (< 1 second) retrieves the most related matches from the PDB. The unit cell matches are combined with sequence alignments using a novel Family Clustering Algorithm to display the results in a user-friendly way. The software tool, Nearest-cell, has been incorporated into the X-ray data collection pipeline at the Diamond Light Source, and is also available as a public web service. The bulk of the thesis is devoted to the study and prediction of protein disorder. Initially, trying to update and extend an existing predictor, RONN, the limitations of the method were exposed and a novel predictor (called MoreRONN) was developed that incorporates a novel sequence-based clustering approach to disorder data inferred from the PDB and DisProt. MoreRONN is now clearly the best-in-class disorder predictor and will soon be offered as a public web service. The third project explores the development of a clustering algorithm for protein structural fragments that can work on the scale of the whole PDB. While protein structures have long been clustered into loose families, there has to date been no comprehensive analytical clustering of short (~6 residue) fragments. A novel fragment clustering tool was built that is now leading to a public database of fragment families and representative structural fragments that should prove extremely helpful for both basic understanding and experimentation. Together, these three projects exemplify how cutting-edge computational approaches applied to extensive protein structure libraries can provide user-friendly tools that address critical everyday issues for structural biologists.